library(renv)
library(tidyverse)
library(broom)
library(labelled)
library(psych)
library(GPArotation)
#library(devtools)
#install_github("cran/multicon") # not on CRAN atm
library(multicon)
library(correlation)
library(careless)
library(corrplot)
library(lavaan)
library(semTools)
library(semPlot)
library(knitr)
library(ggdist)
library(ggforce)
library(cowplot)
library(nortest)
library(lmerTest)source("clean_data.R")Reshape data
df_sbsa_wide_wb <- df_sbsa %>%
arrange(pid, time) %>%
select(pid, time, rando, starts_with(c("sw06", "ml01", "rs01", "sc01"))) %>%
pivot_wider(names_from = time,
names_sep = "_t",
values_from = starts_with(c("sw06", "ml01", "rs01", "sc01")))
# colnames(df_sbsa_wide_wb)Check CFA item loadings at T1
cfa_meaning <- '
# Define the latent factors
meaning1 =~ NA*ml01_01_t1 + lambda1*ml01_01_t1 + lambda2*ml01_02_t1 + lambda3*ml01_03_t1 + lambda4*ml01_04_t1 + lambda5*ml01_05_t1 + lambda6*ml01_06_t1 + lambda7*ml01_07_t1 + lambda8*ml01_08_t1 + lambda9*ml01_09_t1 + lambda10*ml01_10_t1
# Intercepts
ml01_01_t1 ~ i1*1
ml01_02_t1 ~ 1
ml01_03_t1 ~ 1
ml01_04_t1 ~ 1
ml01_05_t1 ~ 1
ml01_06_t1 ~ 1
ml01_07_t1 ~ 1
ml01_08_t1 ~ 1
ml01_09_t1 ~ 1
ml01_10_t1 ~ 1
# Unique Variances
ml01_01_t1 ~~ ml01_01_t1
ml01_02_t1 ~~ ml01_02_t1
ml01_03_t1 ~~ ml01_03_t1
ml01_04_t1 ~~ ml01_04_t1
ml01_05_t1 ~~ ml01_05_t1
ml01_06_t1 ~~ ml01_06_t1
ml01_07_t1 ~~ ml01_07_t1
ml01_08_t1 ~~ ml01_08_t1
ml01_09_t1 ~~ ml01_09_t1
ml01_10_t1 ~~ ml01_10_t1
# Latent Variable Means
meaning1 ~ 0*1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
'
fit_cfa_meaning <- cfa(cfa_meaning, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_cfa_meaning, fit.measures = TRUE)lavaan 0.6.15 ended normally after 40 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 30
Used Total
Number of observations 601 619
Number of missing patterns 2
Model Test User Model:
Test statistic 1908.542
Degrees of freedom 35
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 4083.509
Degrees of freedom 45
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.536
Tucker-Lewis Index (TLI) 0.404
Robust Comparative Fit Index (CFI) 0.536
Robust Tucker-Lewis Index (TLI) 0.403
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -10459.519
Loglikelihood unrestricted model (H1) -9505.247
Akaike (AIC) 20979.037
Bayesian (BIC) 21110.995
Sample-size adjusted Bayesian (SABIC) 21015.753
Root Mean Square Error of Approximation:
RMSEA 0.298
90 Percent confidence interval - lower 0.287
90 Percent confidence interval - upper 0.310
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.298
90 Percent confidence interval - lower 0.287
90 Percent confidence interval - upper 0.310
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.240
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
meaning1 =~
m01_01_ (lmb1) 1.350 0.057 23.719 0.000
m01_02_ (lmb2) -0.393 0.063 -6.275 0.000
m01_03_ (lmb3) -0.186 0.068 -2.738 0.006
m01_04_ (lmb4) 1.549 0.057 27.337 0.000
m01_05_ (lmb5) 1.362 0.058 23.476 0.000
m01_06_ (lmb6) 1.451 0.058 24.994 0.000
m01_07_ (lmb7) -0.191 0.065 -2.954 0.003
m01_08_ (lmb8) -0.331 0.069 -4.776 0.000
m01_09_ (lmb9) -1.435 0.066 -21.800 0.000
m01_10_ (lm10) -0.690 0.069 -9.969 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.ml01_01_1 (i1) 4.201 0.068 62.074 0.000
.ml01_02_1 5.378 0.061 88.527 0.000
.ml01_03_1 5.023 0.064 78.011 0.000
.ml01_04_1 3.847 0.071 54.225 0.000
.ml01_05_1 4.401 0.069 64.031 0.000
.ml01_06_1 3.890 0.070 55.311 0.000
.ml01_07_1 5.017 0.062 81.284 0.000
.ml01_08_1 4.948 0.066 74.644 0.000
.ml01_09_1 3.877 0.076 50.907 0.000
.ml01_10_1 4.894 0.069 71.179 0.000
meaning1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.ml01_01_t1 0.930 0.065 14.416 0.000
.ml01_02_t1 2.060 0.120 17.180 0.000
.ml01_03_t1 2.458 0.142 17.307 0.000
.ml01_04_t1 0.625 0.055 11.469 0.000
.ml01_05_t1 0.984 0.068 14.493 0.000
.ml01_06_t1 0.866 0.063 13.702 0.000
.ml01_07_t1 2.253 0.130 17.303 0.000
.ml01_08_t1 2.532 0.147 17.247 0.000
.ml01_09_t1 1.425 0.094 15.182 0.000
.ml01_10_t1 2.365 0.140 16.943 0.000
meaning1 1.000 tidy(fit_cfa_meaning) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading))# A tibble: 10 × 11
term op label estimate std.error statistic p.value std.lv std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 meaning1 =~… =~ lamb… 1.55 0.0567 27.3 0 1.55 0.891
2 meaning1 =~… =~ lamb… 1.45 0.0581 25.0 0 1.45 0.842
3 meaning1 =~… =~ lamb… -1.44 0.0658 -21.8 0 -1.44 -0.769
4 meaning1 =~… =~ lamb… 1.36 0.0580 23.5 0 1.36 0.808
5 meaning1 =~… =~ lamb… 1.35 0.0569 23.7 0 1.35 0.814
6 meaning1 =~… =~ lamb… -0.690 0.0692 -9.97 0 -0.690 -0.409
7 meaning1 =~… =~ lamb… -0.393 0.0627 -6.28 3.49e-10 -0.393 -0.264
8 meaning1 =~… =~ lamb… -0.331 0.0693 -4.78 1.79e- 6 -0.331 -0.204
9 meaning1 =~… =~ lamb… -0.191 0.0648 -2.95 3.14e- 3 -0.191 -0.127
10 meaning1 =~… =~ lamb… -0.186 0.0678 -2.74 6.18e- 3 -0.186 -0.118
# ℹ 2 more variables: std.nox <dbl>, abs_loading <dbl>Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_wb <- df_sbsa_wide_wb %>%
mutate(ml01_09_t1_r = ml01_09_t1,
ml01_10_t1_r = ml01_10_t1,
ml01_02_t1_r = ml01_02_t1,
ml01_08_t1_r = ml01_08_t1,
ml01_07_t1_r = ml01_07_t1,
ml01_03_t1_r = ml01_03_t1,
ml01_09_t2_r = ml01_09_t2,
ml01_10_t2_r = ml01_10_t2,
ml01_02_t2_r = ml01_02_t2,
ml01_08_t2_r = ml01_08_t2,
ml01_07_t2_r = ml01_07_t2,
ml01_03_t2_r = ml01_03_t2) %>%
mutate(across(intersect(starts_with("ml01"), ends_with("_r")),
~ recode(.x, `1` = 7L, `2` = 6L, `3` = 5L, `4` = 4L, `5` = 3L, `6` = 2L, `7` = 1L, .default = NA_integer_))) %>%
mutate(meaning_par1_t1 = rowMeans(across(c(ml01_04_t1, ml01_10_t1_r, ml01_07_t1_r, ml01_03_t1_r)), na.rm=T),
meaning_par2_t1 = rowMeans(across(c(ml01_06_t1, ml01_01_t1, ml01_08_t1_r)), na.rm=T),
meaning_par3_t1 = rowMeans(across(c(ml01_09_t1_r, ml01_05_t1, ml01_02_t1_r)), na.rm=T),
meaning_par1_t2 = rowMeans(across(c(ml01_04_t2, ml01_10_t2_r, ml01_07_t2_r, ml01_03_t2_r)), na.rm=T),
meaning_par2_t2 = rowMeans(across(c(ml01_06_t2, ml01_01_t2, ml01_08_t2_r)), na.rm=T),
meaning_par3_t2 = rowMeans(across(c(ml01_09_t2_r, ml01_05_t2, ml01_02_t2_r)), na.rm=T))Check CFA item loadings at T1
cfa_selfes <- '
# Define the latent factors
selfes1 =~ NA*rs01_01_t1 + lambda1*rs01_01_t1 + lambda2*rs01_02_t1 + lambda3*rs01_03_t1 + lambda4*rs01_04_t1 + lambda5*rs01_05_t1 + lambda6*rs01_06_t1 + lambda7*rs01_07_t1 + lambda8*rs01_08_t1 + lambda9*rs01_09_t1 + lambda10*rs01_10_t1
# Intercepts
rs01_01_t1 ~ i1*1
rs01_02_t1 ~ 1
rs01_03_t1 ~ 1
rs01_04_t1 ~ 1
rs01_05_t1 ~ 1
rs01_06_t1 ~ 1
rs01_07_t1 ~ 1
rs01_08_t1 ~ 1
rs01_09_t1 ~ 1
rs01_10_t1 ~ 1
# Unique Variances
rs01_01_t1 ~~ rs01_01_t1
rs01_02_t1 ~~ rs01_02_t1
rs01_03_t1 ~~ rs01_03_t1
rs01_04_t1 ~~ rs01_04_t1
rs01_05_t1 ~~ rs01_05_t1
rs01_06_t1 ~~ rs01_06_t1
rs01_07_t1 ~~ rs01_07_t1
rs01_08_t1 ~~ rs01_08_t1
rs01_09_t1 ~~ rs01_09_t1
rs01_10_t1 ~~ rs01_10_t1
# Latent Variable Means
selfes1 ~ 0*1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
'
fit_cfa_selfes <- cfa(cfa_selfes, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_cfa_selfes, fit.measures = TRUE)lavaan 0.6.15 ended normally after 15 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 30
Used Total
Number of observations 601 619
Number of missing patterns 1
Model Test User Model:
Test statistic 464.368
Degrees of freedom 35
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 3194.883
Degrees of freedom 45
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.864
Tucker-Lewis Index (TLI) 0.825
Robust Comparative Fit Index (CFI) 0.864
Robust Tucker-Lewis Index (TLI) 0.825
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -8264.512
Loglikelihood unrestricted model (H1) -8032.328
Akaike (AIC) 16589.024
Bayesian (BIC) 16720.982
Sample-size adjusted Bayesian (SABIC) 16625.740
Root Mean Square Error of Approximation:
RMSEA 0.143
90 Percent confidence interval - lower 0.131
90 Percent confidence interval - upper 0.155
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.143
90 Percent confidence interval - lower 0.131
90 Percent confidence interval - upper 0.155
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.065
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
selfes1 =~
r01_01_ (lmb1) 0.897 0.045 20.103 0.000
r01_02_ (lmb2) -0.954 0.049 -19.273 0.000
r01_03_ (lmb3) 0.552 0.036 15.434 0.000
r01_04_ (lmb4) 0.658 0.039 16.675 0.000
r01_05_ (lmb5) -0.890 0.048 -18.547 0.000
r01_06_ (lmb6) -1.039 0.048 -21.547 0.000
r01_07_ (lmb7) 0.721 0.041 17.606 0.000
r01_08_ (lmb8) -0.559 0.050 -11.271 0.000
r01_09_ (lmb9) -1.110 0.047 -23.715 0.000
r01_10_ (lm10) 1.023 0.042 24.609 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.rs01_01_1 (i1) 2.952 0.050 58.692 0.000
.rs01_02_1 3.349 0.055 61.011 0.000
.rs01_03_1 3.942 0.038 104.394 0.000
.rs01_04_1 3.772 0.042 89.072 0.000
.rs01_05_1 2.885 0.053 54.479 0.000
.rs01_06_1 3.324 0.055 60.195 0.000
.rs01_07_1 3.637 0.044 81.871 0.000
.rs01_08_1 3.852 0.050 76.749 0.000
.rs01_09_1 2.692 0.056 48.476 0.000
.rs01_10_1 3.181 0.050 63.761 0.000
selfes1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.rs01_01_t1 0.715 0.046 15.490 0.000
.rs01_02_t1 0.902 0.058 15.468 0.000
.rs01_03_t1 0.552 0.034 16.295 0.000
.rs01_04_t1 0.645 0.040 16.146 0.000
.rs01_05_t1 0.893 0.056 15.889 0.000
.rs01_06_t1 0.753 0.051 14.661 0.000
.rs01_07_t1 0.667 0.042 15.914 0.000
.rs01_08_t1 1.202 0.071 16.905 0.000
.rs01_09_t1 0.621 0.044 14.096 0.000
.rs01_10_t1 0.451 0.034 13.448 0.000
selfes1 1.000 tidy(fit_cfa_selfes) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading))# A tibble: 10 × 11
term op label estimate std.error statistic p.value std.lv std.all std.nox
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 self… =~ lamb… -1.11 0.0468 -23.7 0 -1.11 -0.816 -0.816
2 self… =~ lamb… -1.04 0.0482 -21.5 0 -1.04 -0.767 -0.767
3 self… =~ lamb… 1.02 0.0416 24.6 0 1.02 0.836 0.836
4 self… =~ lamb… -0.954 0.0495 -19.3 0 -0.954 -0.709 -0.709
5 self… =~ lamb… 0.897 0.0446 20.1 0 0.897 0.728 0.728
6 self… =~ lamb… -0.890 0.0480 -18.5 0 -0.890 -0.686 -0.686
7 self… =~ lamb… 0.721 0.0409 17.6 0 0.721 0.662 0.662
8 self… =~ lamb… 0.658 0.0394 16.7 0 0.658 0.634 0.634
9 self… =~ lamb… -0.559 0.0496 -11.3 0 -0.559 -0.454 -0.454
10 self… =~ lamb… 0.552 0.0358 15.4 0 0.552 0.597 0.597
# ℹ 1 more variable: abs_loading <dbl>Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_wb <- df_sbsa_wide_wb %>%
mutate(rs01_09_t1_r = rs01_09_t1,
rs01_06_t1_r = rs01_06_t1,
rs01_02_t1_r = rs01_02_t1,
rs01_05_t1_r = rs01_05_t1,
rs01_08_t1_r = rs01_08_t1,
rs01_09_t2_r = rs01_09_t2,
rs01_06_t2_r = rs01_06_t2,
rs01_02_t2_r = rs01_02_t2,
rs01_05_t2_r = rs01_05_t2,
rs01_08_t2_r = rs01_08_t2) %>%
mutate(across(intersect(starts_with("rs01"), ends_with("_r")),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(selfes_par1_t1 = rowMeans(across(c(rs01_09_t1_r, rs01_05_t1_r, rs01_08_t1_r, rs01_03_t1)), na.rm=T),
selfes_par2_t1 = rowMeans(across(c(rs01_06_t1_r, rs01_01_t1, rs01_04_t1)), na.rm=T),
selfes_par3_t1 = rowMeans(across(c(rs01_10_t1, rs01_02_t1_r, rs01_07_t1)), na.rm=T),
selfes_par1_t2 = rowMeans(across(c(rs01_09_t2_r, rs01_05_t2_r, rs01_08_t2_r, rs01_03_t2)), na.rm=T),
selfes_par2_t2 = rowMeans(across(c(rs01_06_t2_r, rs01_01_t2, rs01_04_t2)), na.rm=T),
selfes_par3_t2 = rowMeans(across(c(rs01_10_t2, rs01_02_t2_r, rs01_07_t2)), na.rm=T))Check CFA item loadings at T1
cfa_concept <- '
# Define the latent factors
concept1 =~ NA*sc01_01_t1 + lambda1*sc01_01_t1 + lambda2*sc01_02_t1 + lambda3*sc01_03_t1 + lambda4*sc01_04_t1 + lambda5*sc01_05_t1 + lambda6*sc01_06_t1 + lambda7*sc01_07_t1 + lambda8*sc01_08_t1 + lambda9*sc01_09_t1 + lambda10*sc01_10_t1 + lambda11*sc01_11_t1 + lambda12*sc01_12_t1
# Intercepts
sc01_01_t1 ~ i1*1
sc01_02_t1 ~ 1
sc01_03_t1 ~ 1
sc01_04_t1 ~ 1
sc01_05_t1 ~ 1
sc01_06_t1 ~ 1
sc01_07_t1 ~ 1
sc01_08_t1 ~ 1
sc01_09_t1 ~ 1
sc01_10_t1 ~ 1
sc01_11_t1 ~ 1
sc01_12_t1 ~ 1
# Unique Variances
sc01_01_t1 ~~ sc01_01_t1
sc01_02_t1 ~~ sc01_02_t1
sc01_03_t1 ~~ sc01_03_t1
sc01_04_t1 ~~ sc01_04_t1
sc01_05_t1 ~~ sc01_05_t1
sc01_06_t1 ~~ sc01_06_t1
sc01_07_t1 ~~ sc01_07_t1
sc01_08_t1 ~~ sc01_08_t1
sc01_09_t1 ~~ sc01_09_t1
sc01_10_t1 ~~ sc01_10_t1
sc01_11_t1 ~~ sc01_11_t1
sc01_12_t1 ~~ sc01_12_t1
# Latent Variable Means
concept1 ~ 0*1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
'
fit_cfa_concept <- cfa(cfa_concept, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_cfa_concept, fit.measures = TRUE)lavaan 0.6.15 ended normally after 16 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 601 619
Number of missing patterns 1
Model Test User Model:
Test statistic 399.547
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 2957.132
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.880
Tucker-Lewis Index (TLI) 0.854
Robust Comparative Fit Index (CFI) 0.880
Robust Tucker-Lewis Index (TLI) 0.854
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -10761.056
Loglikelihood unrestricted model (H1) -10561.282
Akaike (AIC) 21594.112
Bayesian (BIC) 21752.462
Sample-size adjusted Bayesian (SABIC) 21638.171
Root Mean Square Error of Approximation:
RMSEA 0.103
90 Percent confidence interval - lower 0.094
90 Percent confidence interval - upper 0.113
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.103
90 Percent confidence interval - lower 0.094
90 Percent confidence interval - upper 0.113
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.052
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
concept1 =~
s01_01_ (lmb1) 0.869 0.046 18.884 0.000
s01_02_ (lmb2) 0.987 0.049 20.303 0.000
s01_03_ (lmb3) 0.752 0.050 15.168 0.000
s01_04_ (lmb4) 0.836 0.050 16.813 0.000
s01_05_ (lmb5) 0.772 0.052 14.766 0.000
s01_06_ (lmb6) 0.063 0.051 1.234 0.217
s01_07_ (lmb7) 0.741 0.053 14.024 0.000
s01_08_ (lmb8) 1.010 0.045 22.560 0.000
s01_09_ (lmb9) 1.043 0.048 21.729 0.000
s01_10_ (lm10) 0.851 0.051 16.630 0.000
s01_11_ (lm11) -0.704 0.044 -15.828 0.000
s01_12_ (lm12) 0.834 0.049 16.919 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.sc01_01_1 (i1) 3.228 0.051 63.692 0.000
.sc01_02_1 3.448 0.055 63.207 0.000
.sc01_03_1 3.354 0.052 64.481 0.000
.sc01_04_1 3.255 0.053 61.123 0.000
.sc01_05_1 2.958 0.055 54.204 0.000
.sc01_06_1 2.900 0.048 60.211 0.000
.sc01_07_1 2.762 0.055 50.396 0.000
.sc01_08_1 2.727 0.052 52.537 0.000
.sc01_09_1 2.817 0.055 51.189 0.000
.sc01_10_1 2.935 0.055 53.765 0.000
.sc01_11_1 3.206 0.047 68.112 0.000
.sc01_12_1 3.092 0.053 58.407 0.000
concept1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.sc01_01_t1 0.788 0.051 15.439 0.000
.sc01_02_t1 0.814 0.055 14.929 0.000
.sc01_03_t1 1.062 0.065 16.295 0.000
.sc01_04_t1 1.004 0.063 15.929 0.000
.sc01_05_t1 1.195 0.073 16.353 0.000
.sc01_06_t1 1.390 0.080 17.330 0.000
.sc01_07_t1 1.256 0.076 16.515 0.000
.sc01_08_t1 0.599 0.043 13.966 0.000
.sc01_09_t1 0.732 0.051 14.431 0.000
.sc01_10_t1 1.067 0.067 15.925 0.000
.sc01_11_t1 0.836 0.052 16.163 0.000
.sc01_12_t1 0.988 0.062 15.961 0.000
concept1 1.000 tidy(fit_cfa_concept) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading))# A tibble: 12 × 11
term op label estimate std.error statistic p.value std.lv std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 concept1 =~… =~ lamb… 1.04 0.0480 21.7 0 1.04 0.773
2 concept1 =~… =~ lamb… 1.01 0.0448 22.6 0 1.01 0.794
3 concept1 =~… =~ lamb… 0.987 0.0486 20.3 0 0.987 0.738
4 concept1 =~… =~ lamb… 0.869 0.0460 18.9 0 0.869 0.700
5 concept1 =~… =~ lamb… 0.851 0.0512 16.6 0 0.851 0.636
6 concept1 =~… =~ lamb… 0.836 0.0497 16.8 0 0.836 0.641
7 concept1 =~… =~ lamb… 0.834 0.0493 16.9 0 0.834 0.643
8 concept1 =~… =~ lamb… 0.772 0.0523 14.8 0 0.772 0.577
9 concept1 =~… =~ lamb… 0.752 0.0495 15.2 0 0.752 0.589
10 concept1 =~… =~ lamb… 0.741 0.0528 14.0 0 0.741 0.551
11 concept1 =~… =~ lamb… -0.704 0.0445 -15.8 0 -0.704 -0.610
12 concept1 =~… =~ lamb… 0.0625 0.0507 1.23 0.217 0.0625 0.0529
# ℹ 2 more variables: std.nox <dbl>, abs_loading <dbl>Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_wb <- df_sbsa_wide_wb %>% # based on the item content, it makes more sense here to recode all except sc01_11
mutate(sc01_01_t1_r = sc01_01_t1,
sc01_02_t1_r = sc01_02_t1,
sc01_03_t1_r = sc01_03_t1,
sc01_04_t1_r = sc01_04_t1,
sc01_05_t1_r = sc01_05_t1,
sc01_06_t1_r = sc01_06_t1,
sc01_07_t1_r = sc01_07_t1,
sc01_08_t1_r = sc01_08_t1,
sc01_09_t1_r = sc01_09_t1,
sc01_10_t1_r = sc01_10_t1,
sc01_12_t1_r = sc01_12_t1,
sc01_01_t2_r = sc01_01_t2,
sc01_02_t2_r = sc01_02_t2,
sc01_03_t2_r = sc01_03_t2,
sc01_04_t2_r = sc01_04_t2,
sc01_05_t2_r = sc01_05_t2,
sc01_06_t2_r = sc01_06_t2,
sc01_07_t2_r = sc01_07_t2,
sc01_08_t2_r = sc01_08_t2,
sc01_09_t2_r = sc01_09_t2,
sc01_10_t2_r = sc01_10_t2,
sc01_12_t2_r = sc01_12_t2) %>%
mutate(across(intersect(starts_with("sc01"), ends_with("_r")),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(concept_par1_t1 = rowMeans(across(c(sc01_09_t1_r, sc01_04_t1_r, sc01_03_t1_r, sc01_06_t1_r)), na.rm=T),
concept_par2_t1 = rowMeans(across(c(sc01_08_t1_r, sc01_10_t1_r, sc01_05_t1_r, sc01_11_t1)), na.rm=T),
concept_par3_t1 = rowMeans(across(c(sc01_02_t1_r, sc01_01_t1_r, sc01_12_t1_r, sc01_07_t1_r)), na.rm=T),
concept_par1_t2 = rowMeans(across(c(sc01_09_t2_r, sc01_04_t2_r, sc01_03_t2_r, sc01_06_t2_r)), na.rm=T),
concept_par2_t2 = rowMeans(across(c(sc01_08_t2_r, sc01_10_t2_r, sc01_05_t2_r, sc01_11_t2)), na.rm=T),
concept_par3_t2 = rowMeans(across(c(sc01_02_t2_r, sc01_01_t2_r, sc01_12_t2_r, sc01_07_t2_r)), na.rm=T))df_sbsa_wide_pers <- df_sbsa %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("bf05", "bf06"))) %>%
pivot_wider(names_from = time,
names_sep = "_t",
values_from = starts_with(c("bf05", "bf06")))
# colnames(df_sbsa_wide_pers)Check CFA item loadings at T1
cfa_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*bf05_01_t1 + lambda1*bf05_01_t1 + lambda2*bf05_06_t1 + lambda3*bf05_11_t1 + lambda4*bf05_16_t1 + lambda5*bf05_21_t1 + lambda6*bf05_26_t1 + lambda7*bf05_31_t1 + lambda8*bf05_36_t1 + lambda9*bf05_41_t1 + lambda10*bf05_46_t1 + lambda11*bf05_51_t1 + lambda12*bf05_56_t1
# Intercepts
bf05_01_t1 ~ i1*1
bf05_06_t1 ~ 1
bf05_11_t1 ~ 1
bf05_16_t1 ~ 1
bf05_21_t1 ~ 1
bf05_26_t1 ~ 1
bf05_31_t1 ~ 1
bf05_36_t1 ~ 1
bf05_41_t1 ~ 1
bf05_46_t1 ~ 1
bf05_51_t1 ~ 1
bf05_56_t1 ~ 1
# Unique Variances
bf05_01_t1 ~~ bf05_01_t1
bf05_06_t1 ~~ bf05_06_t1
bf05_11_t1 ~~ bf05_11_t1
bf05_16_t1 ~~ bf05_16_t1
bf05_21_t1 ~~ bf05_21_t1
bf05_26_t1 ~~ bf05_26_t1
bf05_31_t1 ~~ bf05_31_t1
bf05_36_t1 ~~ bf05_36_t1
bf05_41_t1 ~~ bf05_41_t1
bf05_46_t1 ~~ bf05_46_t1
bf05_51_t1 ~~ bf05_51_t1
bf05_56_t1 ~~ bf05_56_t1
# Latent Variable Means
extra_curr1 ~ 0*1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
'
fit_cfa_extra_curr <- cfa(cfa_extra_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_cfa_extra_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 33 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 609 619
Number of missing patterns 2
Model Test User Model:
Test statistic 583.950
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 2202.791
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.752
Tucker-Lewis Index (TLI) 0.697
Robust Comparative Fit Index (CFI) 0.752
Robust Tucker-Lewis Index (TLI) 0.697
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -10842.748
Loglikelihood unrestricted model (H1) -10550.773
Akaike (AIC) 21757.497
Bayesian (BIC) 21916.322
Sample-size adjusted Bayesian (SABIC) 21802.030
Root Mean Square Error of Approximation:
RMSEA 0.127
90 Percent confidence interval - lower 0.118
90 Percent confidence interval - upper 0.136
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.127
90 Percent confidence interval - lower 0.118
90 Percent confidence interval - upper 0.136
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.075
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
extra_curr1 =~
b05_01_ (lmb1) 0.920 0.048 19.020 0.000
b05_06_ (lmb2) 0.546 0.046 11.929 0.000
b05_11_ (lmb3) -0.324 0.050 -6.473 0.000
b05_16_ (lmb4) -0.672 0.046 -14.449 0.000
b05_21_ (lmb5) 0.711 0.052 13.752 0.000
b05_26_ (lmb6) -0.591 0.056 -10.546 0.000
b05_31_ (lmb7) -0.704 0.042 -16.598 0.000
b05_36_ (lmb8) -0.518 0.047 -10.999 0.000
b05_41_ (lmb9) 0.750 0.047 15.814 0.000
b05_46_ (lm10) 0.854 0.051 16.684 0.000
b05_51_ (lm11) -0.567 0.051 -11.196 0.000
b05_56_ (lm12) 0.634 0.045 14.200 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_01_1 (i1) 2.690 0.052 51.737 0.000
.bf05_06_1 3.036 0.045 67.283 0.000
.bf05_11_1 2.670 0.047 56.898 0.000
.bf05_16_1 3.837 0.046 82.994 0.000
.bf05_21_1 2.665 0.052 51.435 0.000
.bf05_26_1 3.125 0.054 58.000 0.000
.bf05_31_1 4.046 0.044 92.426 0.000
.bf05_36_1 3.166 0.046 68.934 0.000
.bf05_41_1 2.848 0.048 58.902 0.000
.bf05_46_1 2.962 0.053 55.829 0.000
.bf05_51_1 3.174 0.049 64.449 0.000
.bf05_56_1 3.222 0.045 71.975 0.000
extr_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_01_t1 0.800 0.057 14.102 0.000
.bf05_06_t1 0.942 0.057 16.460 0.000
.bf05_11_t1 1.236 0.072 17.175 0.000
.bf05_16_t1 0.851 0.056 15.317 0.000
.bf05_21_t1 1.129 0.071 15.953 0.000
.bf05_26_t1 1.419 0.086 16.564 0.000
.bf05_31_t1 0.672 0.045 14.826 0.000
.bf05_36_t1 1.016 0.061 16.611 0.000
.bf05_41_t1 0.859 0.057 15.000 0.000
.bf05_46_t1 0.985 0.066 14.901 0.000
.bf05_51_t1 1.156 0.070 16.504 0.000
.bf05_56_t1 0.819 0.052 15.724 0.000
extra_curr1 1.000 tidy(fit_cfa_extra_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading))# A tibble: 12 × 11
term op label estimate std.error statistic p.value std.lv std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 extra_curr1… =~ lamb… 0.920 0.0484 19.0 0 0.920 0.717
2 extra_curr1… =~ lamb… 0.854 0.0512 16.7 0 0.854 0.652
3 extra_curr1… =~ lamb… 0.750 0.0474 15.8 0 0.750 0.629
4 extra_curr1… =~ lamb… 0.711 0.0517 13.8 0 0.711 0.556
5 extra_curr1… =~ lamb… -0.704 0.0424 -16.6 0 -0.704 -0.651
6 extra_curr1… =~ lamb… -0.672 0.0465 -14.4 0 -0.672 -0.589
7 extra_curr1… =~ lamb… 0.634 0.0446 14.2 0 0.634 0.574
8 extra_curr1… =~ lamb… -0.591 0.0560 -10.5 0 -0.591 -0.444
9 extra_curr1… =~ lamb… -0.567 0.0506 -11.2 0 -0.567 -0.466
10 extra_curr1… =~ lamb… 0.546 0.0458 11.9 0 0.546 0.490
11 extra_curr1… =~ lamb… -0.518 0.0471 -11.0 0 -0.518 -0.457
12 extra_curr1… =~ lamb… -0.324 0.0500 -6.47 9.63e-11 -0.324 -0.280
# ℹ 2 more variables: std.nox <dbl>, abs_loading <dbl>Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf05_31_t1_r = bf05_31_t1,
bf05_16_t1_r = bf05_16_t1,
bf05_26_t1_r = bf05_26_t1,
bf05_51_t1_r = bf05_51_t1,
bf05_36_t1_r = bf05_36_t1,
bf05_11_t1_r = bf05_11_t1,
bf05_31_t2_r = bf05_31_t2,
bf05_16_t2_r = bf05_16_t2,
bf05_26_t2_r = bf05_26_t2,
bf05_51_t2_r = bf05_51_t2,
bf05_36_t2_r = bf05_36_t2,
bf05_11_t2_r = bf05_11_t2) %>%
mutate(across(c(bf05_31_t1_r, bf05_16_t1_r, bf05_26_t1_r, bf05_51_t1_r, bf05_36_t1_r, bf05_11_t1_r,
bf05_31_t2_r, bf05_16_t2_r, bf05_26_t2_r, bf05_51_t2_r, bf05_36_t2_r, bf05_11_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(extra_curr_par1_t1 = rowMeans(across(c(bf05_01_t1, bf05_16_t1_r, bf05_51_t1_r, bf05_11_t1_r)), na.rm=T),
extra_curr_par2_t1 = rowMeans(across(c(bf05_46_t1, bf05_31_t1_r, bf05_26_t1_r, bf05_36_t1_r)), na.rm=T),
extra_curr_par3_t1 = rowMeans(across(c(bf05_41_t1, bf05_21_t1, bf05_56_t1, bf05_06_t1)), na.rm=T),
extra_curr_par1_t2 = rowMeans(across(c(bf05_01_t2, bf05_16_t2_r, bf05_51_t2_r, bf05_11_t2_r)), na.rm=T),
extra_curr_par2_t2 = rowMeans(across(c(bf05_46_t2, bf05_31_t2_r, bf05_26_t2_r, bf05_36_t2_r)), na.rm=T),
extra_curr_par3_t2 = rowMeans(across(c(bf05_41_t2, bf05_21_t2, bf05_56_t2, bf05_06_t2)), na.rm=T))Check CFA item loadings at T1
cfa_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*bf06_01_t1 + lambda1*bf06_01_t1 + lambda2*bf06_06_t1 + lambda3*bf06_11_t1 + lambda4*bf06_16_t1 + lambda5*bf06_21_t1 + lambda6*bf06_26_t1 + lambda7*bf06_31_t1 + lambda8*bf06_36_t1 + lambda9*bf06_41_t1 + lambda10*bf06_46_t1 + lambda11*bf06_51_t1 + lambda12*bf06_56_t1
# Intercepts
bf06_01_t1 ~ i1*1
bf06_06_t1 ~ 1
bf06_11_t1 ~ 1
bf06_16_t1 ~ 1
bf06_21_t1 ~ 1
bf06_26_t1 ~ 1
bf06_31_t1 ~ 1
bf06_36_t1 ~ 1
bf06_41_t1 ~ 1
bf06_46_t1 ~ 1
bf06_51_t1 ~ 1
bf06_56_t1 ~ 1
# Unique Variances
bf06_01_t1 ~~ bf06_01_t1
bf06_06_t1 ~~ bf06_06_t1
bf06_11_t1 ~~ bf06_11_t1
bf06_16_t1 ~~ bf06_16_t1
bf06_21_t1 ~~ bf06_21_t1
bf06_26_t1 ~~ bf06_26_t1
bf06_31_t1 ~~ bf06_31_t1
bf06_36_t1 ~~ bf06_36_t1
bf06_41_t1 ~~ bf06_41_t1
bf06_46_t1 ~~ bf06_46_t1
bf06_51_t1 ~~ bf06_51_t1
bf06_56_t1 ~~ bf06_56_t1
# Latent Variable Means
extra_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
'
fit_cfa_extra_ideal <- cfa(cfa_extra_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_cfa_extra_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 18 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 609 619
Number of missing patterns 1
Model Test User Model:
Test statistic 272.217
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 971.578
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.759
Tucker-Lewis Index (TLI) 0.705
Robust Comparative Fit Index (CFI) 0.759
Robust Tucker-Lewis Index (TLI) 0.705
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -10004.455
Loglikelihood unrestricted model (H1) -9868.347
Akaike (AIC) 20080.911
Bayesian (BIC) 20239.736
Sample-size adjusted Bayesian (SABIC) 20125.444
Root Mean Square Error of Approximation:
RMSEA 0.081
90 Percent confidence interval - lower 0.072
90 Percent confidence interval - upper 0.091
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.610
Robust RMSEA 0.081
90 Percent confidence interval - lower 0.072
90 Percent confidence interval - upper 0.091
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.610
Standardized Root Mean Square Residual:
SRMR 0.058
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
extra_ideal1 =~
b06_01_ (lmb1) 0.312 0.036 8.578 0.000
b06_06_ (lmb2) 0.333 0.045 7.479 0.000
b06_11_ (lmb3) -0.263 0.065 -4.054 0.000
b06_16_ (lmb4) -0.500 0.055 -9.081 0.000
b06_21_ (lmb5) 0.498 0.050 10.025 0.000
b06_26_ (lmb6) -0.482 0.042 -11.509 0.000
b06_31_ (lmb7) -0.537 0.048 -11.182 0.000
b06_36_ (lmb8) -0.470 0.048 -9.759 0.000
b06_41_ (lmb9) 0.397 0.033 11.985 0.000
b06_46_ (lm10) 0.282 0.050 5.618 0.000
b06_51_ (lm11) -0.516 0.047 -10.947 0.000
b06_56_ (lm12) 0.362 0.037 9.874 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_01_1 (i1) 4.241 0.032 132.589 0.000
.bf06_06_1 4.080 0.039 103.807 0.000
.bf06_11_1 2.402 0.056 42.984 0.000
.bf06_16_1 2.887 0.048 59.976 0.000
.bf06_21_1 3.770 0.044 85.223 0.000
.bf06_26_1 1.700 0.038 44.873 0.000
.bf06_31_1 2.166 0.043 50.204 0.000
.bf06_36_1 2.189 0.043 50.699 0.000
.bf06_41_1 4.442 0.030 147.093 0.000
.bf06_46_1 3.764 0.043 87.258 0.000
.bf06_51_1 2.401 0.043 56.388 0.000
.bf06_56_1 4.235 0.033 129.694 0.000
extra_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_01_t1 0.526 0.032 16.193 0.000
.bf06_06_t1 0.830 0.050 16.600 0.000
.bf06_11_t1 1.833 0.107 17.203 0.000
.bf06_16_t1 1.161 0.073 15.875 0.000
.bf06_21_t1 0.944 0.060 15.663 0.000
.bf06_26_t1 0.641 0.043 14.994 0.000
.bf06_31_t1 0.845 0.056 15.085 0.000
.bf06_36_t1 0.914 0.058 15.891 0.000
.bf06_41_t1 0.397 0.027 14.820 0.000
.bf06_46_t1 1.053 0.062 16.920 0.000
.bf06_51_t1 0.838 0.055 15.368 0.000
.bf06_56_t1 0.518 0.033 15.736 0.000
extra_ideal1 1.000 tidy(fit_cfa_extra_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading))# A tibble: 12 × 11
term op label estimate std.error statistic p.value std.lv std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 extra_ideal… =~ lamb… -0.537 0.0481 -11.2 0 -0.537 -0.505
2 extra_ideal… =~ lamb… -0.516 0.0471 -10.9 0 -0.516 -0.491
3 extra_ideal… =~ lamb… -0.500 0.0551 -9.08 0 -0.500 -0.421
4 extra_ideal… =~ lamb… 0.498 0.0497 10.0 0 0.498 0.456
5 extra_ideal… =~ lamb… -0.482 0.0419 -11.5 0 -0.482 -0.516
6 extra_ideal… =~ lamb… -0.470 0.0482 -9.76 0 -0.470 -0.441
7 extra_ideal… =~ lamb… 0.397 0.0332 12.0 0 0.397 0.533
8 extra_ideal… =~ lamb… 0.362 0.0367 9.87 0 0.362 0.449
9 extra_ideal… =~ lamb… 0.333 0.0446 7.48 7.53e-14 0.333 0.344
10 extra_ideal… =~ lamb… 0.312 0.0363 8.58 0 0.312 0.395
11 extra_ideal… =~ lamb… 0.282 0.0502 5.62 1.93e- 8 0.282 0.265
12 extra_ideal… =~ lamb… -0.263 0.0649 -4.05 5.04e- 5 -0.263 -0.191
# ℹ 2 more variables: std.nox <dbl>, abs_loading <dbl>Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf06_31_t1_r = bf06_31_t1,
bf06_16_t1_r = bf06_16_t1,
bf06_26_t1_r = bf06_26_t1,
bf06_51_t1_r = bf06_51_t1,
bf06_36_t1_r = bf06_36_t1,
bf06_11_t1_r = bf06_11_t1,
bf06_31_t2_r = bf06_31_t2,
bf06_16_t2_r = bf06_16_t2,
bf06_26_t2_r = bf06_26_t2,
bf06_51_t2_r = bf06_51_t2,
bf06_36_t2_r = bf06_36_t2,
bf06_11_t2_r = bf06_11_t2) %>%
mutate(across(c(bf06_31_t1_r, bf06_16_t1_r, bf06_26_t1_r, bf06_51_t1_r, bf06_36_t1_r, bf06_11_t1_r,
bf06_31_t2_r, bf06_16_t2_r, bf06_26_t2_r, bf06_51_t2_r, bf06_36_t2_r, bf06_11_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(extra_ideal_par1_t1 = rowMeans(across(c(bf06_31_t1_r, bf06_36_t1_r, bf06_06_t1, bf06_11_t1_r)), na.rm=T),
extra_ideal_par2_t1 = rowMeans(across(c(bf06_51_t1_r, bf06_26_t1_r, bf06_56_t1, bf06_46_t1)), na.rm=T),
extra_ideal_par3_t1 = rowMeans(across(c(bf06_16_t1_r, bf06_21_t1, bf06_41_t1, bf06_01_t1)), na.rm=T),
extra_ideal_par1_t2 = rowMeans(across(c(bf06_31_t2_r, bf06_36_t2_r, bf06_06_t2, bf06_11_t2_r)), na.rm=T),
extra_ideal_par2_t2 = rowMeans(across(c(bf06_51_t2_r, bf06_26_t2_r, bf06_56_t2, bf06_46_t2)), na.rm=T),
extra_ideal_par3_t2 = rowMeans(across(c(bf06_16_t2_r, bf06_21_t2, bf06_41_t2, bf06_01_t2)), na.rm=T))Check CFA item loadings at T1
cfa_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*bf05_02_t1 + lambda1*bf05_02_t1 + lambda2*bf05_07_t1 + lambda3*bf05_12_t1 + lambda4*bf05_17_t1 + lambda5*bf05_22_t1 + lambda6*bf05_27_t1 + lambda7*bf05_32_t1 + lambda8*bf05_37_t1 + lambda9*bf05_42_t1 + lambda10*bf05_47_t1 + lambda11*bf05_52_t1 + lambda12*bf05_57_t1
# Intercepts
bf05_02_t1 ~ i1*1
bf05_07_t1 ~ 1
bf05_12_t1 ~ 1
bf05_17_t1 ~ 1
bf05_22_t1 ~ 1
bf05_27_t1 ~ 1
bf05_32_t1 ~ 1
bf05_37_t1 ~ 1
bf05_42_t1 ~ 1
bf05_47_t1 ~ 1
bf05_52_t1 ~ 1
bf05_57_t1 ~ 1
# Unique Variances
bf05_02_t1 ~~ bf05_02_t1
bf05_07_t1 ~~ bf05_07_t1
bf05_12_t1 ~~ bf05_12_t1
bf05_17_t1 ~~ bf05_17_t1
bf05_22_t1 ~~ bf05_22_t1
bf05_27_t1 ~~ bf05_27_t1
bf05_32_t1 ~~ bf05_32_t1
bf05_37_t1 ~~ bf05_37_t1
bf05_42_t1 ~~ bf05_42_t1
bf05_47_t1 ~~ bf05_47_t1
bf05_52_t1 ~~ bf05_52_t1
bf05_57_t1 ~~ bf05_57_t1
# Latent Variable Means
agree_curr1 ~ 0*1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
'
fit_cfa_agree_curr <- cfa(cfa_agree_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_cfa_agree_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 38 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 609 619
Number of missing patterns 5
Model Test User Model:
Test statistic 340.898
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1566.932
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.809
Tucker-Lewis Index (TLI) 0.766
Robust Comparative Fit Index (CFI) 0.809
Robust Tucker-Lewis Index (TLI) 0.767
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -10405.768
Loglikelihood unrestricted model (H1) -10235.319
Akaike (AIC) 20883.537
Bayesian (BIC) 21042.362
Sample-size adjusted Bayesian (SABIC) 20928.070
Root Mean Square Error of Approximation:
RMSEA 0.093
90 Percent confidence interval - lower 0.084
90 Percent confidence interval - upper 0.103
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.990
Robust RMSEA 0.093
90 Percent confidence interval - lower 0.084
90 Percent confidence interval - upper 0.103
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.990
Standardized Root Mean Square Residual:
SRMR 0.062
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
agree_curr1 =~
b05_02_ (lmb1) 0.536 0.038 14.080 0.000
b05_07_ (lmb2) 0.460 0.031 14.902 0.000
b05_12_ (lmb3) -0.576 0.052 -10.992 0.000
b05_17_ (lmb4) -0.269 0.063 -4.243 0.000
b05_22_ (lmb5) -0.631 0.051 -12.269 0.000
b05_27_ (lmb6) 0.593 0.050 11.760 0.000
b05_32_ (lmb7) 0.470 0.037 12.637 0.000
b05_37_ (lmb8) -0.748 0.052 -14.490 0.000
b05_42_ (lmb9) -0.335 0.051 -6.538 0.000
b05_47_ (lm10) -0.756 0.052 -14.455 0.000
b05_52_ (lm11) 0.483 0.033 14.600 0.000
b05_57_ (lm12) 0.583 0.051 11.464 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_02_1 (i1) 4.143 0.038 110.368 0.000
.bf05_07_1 4.435 0.031 143.894 0.000
.bf05_12_1 2.943 0.050 58.794 0.000
.bf05_17_1 2.796 0.058 48.465 0.000
.bf05_22_1 2.311 0.050 46.542 0.000
.bf05_27_1 3.633 0.048 74.910 0.000
.bf05_32_1 3.938 0.036 108.668 0.000
.bf05_37_1 2.594 0.051 50.762 0.000
.bf05_42_1 3.588 0.047 76.889 0.000
.bf05_47_1 2.461 0.052 47.324 0.000
.bf05_52_1 4.229 0.033 128.738 0.000
.bf05_57_1 3.105 0.049 63.917 0.000
agre_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_02_t1 0.570 0.037 15.301 0.000
.bf05_07_t1 0.366 0.024 15.029 0.000
.bf05_12_t1 1.194 0.073 16.304 0.000
.bf05_17_t1 1.955 0.113 17.303 0.000
.bf05_22_t1 1.102 0.069 15.896 0.000
.bf05_27_t1 1.078 0.067 16.072 0.000
.bf05_32_t1 0.578 0.036 15.889 0.000
.bf05_37_t1 1.031 0.068 15.138 0.000
.bf05_42_t1 1.214 0.071 17.032 0.000
.bf05_47_t1 1.076 0.070 15.292 0.000
.bf05_52_t1 0.423 0.028 15.139 0.000
.bf05_57_t1 1.098 0.068 16.112 0.000
agree_curr1 1.000 tidy(fit_cfa_agree_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading))# A tibble: 12 × 11
term op label estimate std.error statistic p.value std.lv std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 agree_curr1… =~ lamb… -0.756 0.0523 -14.5 0 -0.756 -0.589
2 agree_curr1… =~ lamb… -0.748 0.0516 -14.5 0 -0.748 -0.593
3 agree_curr1… =~ lamb… -0.631 0.0514 -12.3 0 -0.631 -0.515
4 agree_curr1… =~ lamb… 0.593 0.0504 11.8 0 0.593 0.496
5 agree_curr1… =~ lamb… 0.583 0.0508 11.5 0 0.583 0.486
6 agree_curr1… =~ lamb… -0.576 0.0524 -11.0 0 -0.576 -0.466
7 agree_curr1… =~ lamb… 0.536 0.0381 14.1 0 0.536 0.579
8 agree_curr1… =~ lamb… 0.483 0.0331 14.6 0 0.483 0.596
9 agree_curr1… =~ lamb… 0.470 0.0372 12.6 0 0.470 0.526
10 agree_curr1… =~ lamb… 0.460 0.0309 14.9 0 0.460 0.606
11 agree_curr1… =~ lamb… -0.335 0.0512 -6.54 6.23e-11 -0.335 -0.291
12 agree_curr1… =~ lamb… -0.269 0.0633 -4.24 2.20e- 5 -0.269 -0.189
# ℹ 2 more variables: std.nox <dbl>, abs_loading <dbl>Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf05_47_t1_r = bf05_47_t1,
bf05_37_t1_r = bf05_37_t1,
bf05_22_t1_r = bf05_22_t1,
bf05_12_t1_r = bf05_12_t1,
bf05_42_t1_r = bf05_42_t1,
bf05_17_t1_r = bf05_17_t1,
bf05_47_t2_r = bf05_47_t2,
bf05_37_t2_r = bf05_37_t2,
bf05_22_t2_r = bf05_22_t2,
bf05_12_t2_r = bf05_12_t2,
bf05_42_t2_r = bf05_42_t2,
bf05_17_t2_r = bf05_17_t2) %>%
mutate(across(c(bf05_47_t1_r, bf05_37_t1_r, bf05_22_t1_r, bf05_12_t1_r, bf05_42_t1_r, bf05_17_t1_r,
bf05_47_t2_r, bf05_37_t2_r, bf05_22_t2_r, bf05_12_t2_r, bf05_42_t2_r, bf05_17_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(agree_curr_par1_t1 = rowMeans(across(c(bf05_47_t1_r, bf05_12_t1_r, bf05_32_t1, bf05_17_t1)), na.rm=T),
agree_curr_par2_t1 = rowMeans(across(c(bf05_37_t1_r, bf05_57_t1, bf05_52_t1, bf05_42_t1)), na.rm=T),
agree_curr_par3_t1 = rowMeans(across(c(bf05_22_t1_r, bf05_27_t1, bf05_02_t1, bf05_07_t1)), na.rm=T),
agree_curr_par1_t2 = rowMeans(across(c(bf05_47_t2_r, bf05_12_t2_r, bf05_32_t2, bf05_17_t2)), na.rm=T),
agree_curr_par2_t2 = rowMeans(across(c(bf05_37_t2_r, bf05_57_t2, bf05_52_t2, bf05_42_t2)), na.rm=T),
agree_curr_par3_t2 = rowMeans(across(c(bf05_22_t2_r, bf05_27_t2, bf05_02_t2, bf05_07_t2)), na.rm=T))Check CFA item loadings at T1
cfa_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*bf06_02_t1 + lambda1*bf06_02_t1 + lambda2*bf06_07_t1 + lambda3*bf06_12_t1 + lambda4*bf06_17_t1 + lambda5*bf06_22_t1 + lambda6*bf06_27_t1 + lambda7*bf06_32_t1 + lambda8*bf06_37_t1 + lambda9*bf06_42_t1 + lambda10*bf06_47_t1 + lambda11*bf06_52_t1 + lambda12*bf06_57_t1
# Intercepts
bf06_02_t1 ~ i1*1
bf06_07_t1 ~ 1
bf06_12_t1 ~ 1
bf06_17_t1 ~ 1
bf06_22_t1 ~ 1
bf06_27_t1 ~ 1
bf06_32_t1 ~ 1
bf06_37_t1 ~ 1
bf06_42_t1 ~ 1
bf06_47_t1 ~ 1
bf06_52_t1 ~ 1
bf06_57_t1 ~ 1
# Unique Variances
bf06_02_t1 ~~ bf06_02_t1
bf06_07_t1 ~~ bf06_07_t1
bf06_12_t1 ~~ bf06_12_t1
bf06_17_t1 ~~ bf06_17_t1
bf06_22_t1 ~~ bf06_22_t1
bf06_27_t1 ~~ bf06_27_t1
bf06_32_t1 ~~ bf06_32_t1
bf06_37_t1 ~~ bf06_37_t1
bf06_42_t1 ~~ bf06_42_t1
bf06_47_t1 ~~ bf06_47_t1
bf06_52_t1 ~~ bf06_52_t1
bf06_57_t1 ~~ bf06_57_t1
# Latent Variable Means
agree_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
'
fit_cfa_agree_ideal <- cfa(cfa_agree_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_cfa_agree_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 42 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 609 619
Number of missing patterns 3
Model Test User Model:
Test statistic 179.193
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1384.060
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.905
Tucker-Lewis Index (TLI) 0.884
Robust Comparative Fit Index (CFI) 0.905
Robust Tucker-Lewis Index (TLI) 0.884
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -9707.113
Loglikelihood unrestricted model (H1) -9617.516
Akaike (AIC) 19486.225
Bayesian (BIC) 19645.051
Sample-size adjusted Bayesian (SABIC) 19530.759
Root Mean Square Error of Approximation:
RMSEA 0.062
90 Percent confidence interval - lower 0.052
90 Percent confidence interval - upper 0.072
P-value H_0: RMSEA <= 0.050 0.026
P-value H_0: RMSEA >= 0.080 0.001
Robust RMSEA 0.062
90 Percent confidence interval - lower 0.052
90 Percent confidence interval - upper 0.072
P-value H_0: Robust RMSEA <= 0.050 0.026
P-value H_0: Robust RMSEA >= 0.080 0.001
Standardized Root Mean Square Residual:
SRMR 0.043
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
agree_ideal1 =~
b06_02_ (lmb1) 0.520 0.037 14.060 0.000
b06_07_ (lmb2) 0.343 0.029 11.805 0.000
b06_12_ (lmb3) -0.382 0.043 -8.965 0.000
b06_17_ (lmb4) -0.145 0.069 -2.090 0.037
b06_22_ (lmb5) -0.412 0.047 -8.711 0.000
b06_27_ (lmb6) 0.616 0.043 14.369 0.000
b06_32_ (lmb7) 0.485 0.039 12.338 0.000
b06_37_ (lmb8) -0.537 0.038 -14.218 0.000
b06_42_ (lmb9) -0.435 0.051 -8.594 0.000
b06_47_ (lm10) -0.670 0.044 -15.070 0.000
b06_52_ (lm11) 0.465 0.030 15.645 0.000
b06_57_ (lm12) 0.584 0.041 14.190 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_02_1 (i1) 4.256 0.037 116.146 0.000
.bf06_07_1 4.635 0.028 166.038 0.000
.bf06_12_1 1.943 0.040 48.580 0.000
.bf06_17_1 2.787 0.063 44.409 0.000
.bf06_22_1 1.920 0.044 43.295 0.000
.bf06_27_1 3.941 0.043 92.620 0.000
.bf06_32_1 4.280 0.038 111.967 0.000
.bf06_37_1 1.704 0.037 45.484 0.000
.bf06_42_1 2.765 0.047 58.543 0.000
.bf06_47_1 1.865 0.045 41.903 0.000
.bf06_52_1 4.479 0.030 149.271 0.000
.bf06_57_1 3.693 0.041 90.499 0.000
agree_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_02_t1 0.547 0.035 15.467 0.000
.bf06_07_t1 0.357 0.022 16.080 0.000
.bf06_12_t1 0.828 0.049 16.744 0.000
.bf06_17_t1 2.377 0.136 17.415 0.000
.bf06_22_t1 1.027 0.061 16.778 0.000
.bf06_27_t1 0.722 0.047 15.305 0.000
.bf06_32_t1 0.653 0.041 15.988 0.000
.bf06_37_t1 0.566 0.037 15.378 0.000
.bf06_42_t1 1.169 0.070 16.771 0.000
.bf06_47_t1 0.757 0.050 15.020 0.000
.bf06_52_t1 0.332 0.022 14.812 0.000
.bf06_57_t1 0.673 0.044 15.384 0.000
agree_ideal1 1.000 tidy(fit_cfa_agree_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading))# A tibble: 12 × 11
term op label estimate std.error statistic p.value std.lv std.all std.nox
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 agre… =~ lamb… -0.670 0.0445 -15.1 0 -0.670 -0.610 -0.610
2 agre… =~ lamb… 0.616 0.0428 14.4 0 0.616 0.587 0.587
3 agre… =~ lamb… 0.584 0.0412 14.2 0 0.584 0.580 0.580
4 agre… =~ lamb… -0.537 0.0378 -14.2 0 -0.537 -0.581 -0.581
5 agre… =~ lamb… 0.520 0.0370 14.1 0 0.520 0.575 0.575
6 agre… =~ lamb… 0.485 0.0393 12.3 0 0.485 0.515 0.515
7 agre… =~ lamb… 0.465 0.0297 15.6 0 0.465 0.628 0.628
8 agre… =~ lamb… -0.435 0.0506 -8.59 0 -0.435 -0.373 -0.373
9 agre… =~ lamb… -0.412 0.0474 -8.71 0 -0.412 -0.377 -0.377
10 agre… =~ lamb… -0.382 0.0426 -8.96 0 -0.382 -0.387 -0.387
11 agre… =~ lamb… 0.343 0.0290 11.8 0 0.343 0.497 0.497
12 agre… =~ lamb… -0.145 0.0693 -2.09 0.0366 -0.145 -0.0935 -0.0935
# ℹ 1 more variable: abs_loading <dbl>Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf06_47_t1_r = bf06_47_t1,
bf06_37_t1_r = bf06_37_t1,
bf06_22_t1_r = bf06_22_t1,
bf06_12_t1_r = bf06_12_t1,
bf06_42_t1_r = bf06_42_t1,
bf06_17_t1_r = bf06_17_t1,
bf06_47_t2_r = bf06_47_t2,
bf06_37_t2_r = bf06_37_t2,
bf06_22_t2_r = bf06_22_t2,
bf06_12_t2_r = bf06_12_t2,
bf06_42_t2_r = bf06_42_t2,
bf06_17_t2_r = bf06_17_t2) %>%
mutate(across(c(bf06_47_t1_r, bf06_37_t1_r, bf06_22_t1_r, bf06_12_t1_r, bf06_42_t1_r, bf06_17_t1_r,
bf06_47_t2_r, bf06_37_t2_r, bf06_22_t2_r, bf06_12_t2_r, bf06_42_t2_r, bf06_17_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(agree_ideal_par1_t1 = rowMeans(across(c(bf06_47_t1_r, bf06_32_t1, bf06_22_t1_r, bf06_17_t1_r)), na.rm=T),
agree_ideal_par2_t1 = rowMeans(across(c(bf06_27_t1, bf06_02_t1, bf06_42_t1_r, bf06_07_t1)), na.rm=T),
agree_ideal_par3_t1 = rowMeans(across(c(bf06_57_t1, bf06_37_t1_r, bf06_52_t1, bf06_12_t1_r)), na.rm=T),
agree_ideal_par1_t2 = rowMeans(across(c(bf06_47_t2_r, bf06_32_t2, bf06_22_t2_r, bf06_17_t2_r)), na.rm=T),
agree_ideal_par2_t2 = rowMeans(across(c(bf06_27_t2, bf06_02_t2, bf06_42_t2_r, bf06_07_t2)), na.rm=T),
agree_ideal_par3_t2 = rowMeans(across(c(bf06_57_t2, bf06_37_t2_r, bf06_52_t2, bf06_12_t2_r)), na.rm=T))Check CFA item loadings at T1
cfa_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*bf05_03_t1 + lambda1*bf05_03_t1 + lambda2*bf05_08_t1 + lambda3*bf05_13_t1 + lambda4*bf05_18_t1 + lambda5*bf05_23_t1 + lambda6*bf05_28_t1 + lambda7*bf05_33_t1 + lambda8*bf05_38_t1 + lambda9*bf05_43_t1 + lambda10*bf05_48_t1 + lambda11*bf05_53_t1 + lambda12*bf05_58_t1
# Intercepts
bf05_03_t1 ~ i1*1
bf05_08_t1 ~ 1
bf05_13_t1 ~ 1
bf05_18_t1 ~ 1
bf05_23_t1 ~ 1
bf05_28_t1 ~ 1
bf05_33_t1 ~ 1
bf05_38_t1 ~ 1
bf05_43_t1 ~ 1
bf05_48_t1 ~ 1
bf05_53_t1 ~ 1
bf05_58_t1 ~ 1
# Unique Variances
bf05_03_t1 ~~ bf05_03_t1
bf05_08_t1 ~~ bf05_08_t1
bf05_13_t1 ~~ bf05_13_t1
bf05_18_t1 ~~ bf05_18_t1
bf05_23_t1 ~~ bf05_23_t1
bf05_28_t1 ~~ bf05_28_t1
bf05_33_t1 ~~ bf05_33_t1
bf05_38_t1 ~~ bf05_38_t1
bf05_43_t1 ~~ bf05_43_t1
bf05_48_t1 ~~ bf05_48_t1
bf05_53_t1 ~~ bf05_53_t1
bf05_58_t1 ~~ bf05_58_t1
# Latent Variable Means
consc_curr1 ~ 0*1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
'
fit_cfa_consc_curr <- cfa(cfa_consc_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_cfa_consc_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 34 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 609 619
Number of missing patterns 2
Model Test User Model:
Test statistic 454.207
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 2735.550
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.850
Tucker-Lewis Index (TLI) 0.817
Robust Comparative Fit Index (CFI) 0.850
Robust Tucker-Lewis Index (TLI) 0.817
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -10517.936
Loglikelihood unrestricted model (H1) -10290.832
Akaike (AIC) 21107.872
Bayesian (BIC) 21266.697
Sample-size adjusted Bayesian (SABIC) 21152.405
Root Mean Square Error of Approximation:
RMSEA 0.110
90 Percent confidence interval - lower 0.101
90 Percent confidence interval - upper 0.120
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.110
90 Percent confidence interval - lower 0.101
90 Percent confidence interval - upper 0.120
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.060
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
consc_curr1 =~
b05_03_ (lmb1) 1.103 0.052 21.153 0.000
b05_08_ (lmb2) 0.755 0.050 15.232 0.000
b05_13_ (lmb3) -0.435 0.044 -9.924 0.000
b05_18_ (lmb4) -0.907 0.045 -20.383 0.000
b05_23_ (lmb5) 0.808 0.051 15.685 0.000
b05_28_ (lmb6) 0.704 0.050 14.078 0.000
b05_33_ (lmb7) -0.897 0.045 -20.073 0.000
b05_38_ (lmb8) -0.664 0.039 -16.969 0.000
b05_43_ (lmb9) -0.459 0.037 -12.461 0.000
b05_48_ (lm10) 0.783 0.047 16.803 0.000
b05_53_ (lm11) -0.655 0.044 -14.853 0.000
b05_58_ (lm12) 0.675 0.051 13.219 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_03_1 (i1) 2.875 0.059 48.817 0.000
.bf05_08_1 3.445 0.052 66.598 0.000
.bf05_13_1 3.609 0.043 83.617 0.000
.bf05_18_1 3.537 0.050 71.271 0.000
.bf05_23_1 3.310 0.054 61.392 0.000
.bf05_28_1 2.841 0.051 55.276 0.000
.bf05_33_1 3.345 0.050 67.467 0.000
.bf05_38_1 3.580 0.042 86.012 0.000
.bf05_43_1 3.972 0.037 107.060 0.000
.bf05_48_1 2.120 0.050 42.790 0.000
.bf05_53_1 3.667 0.046 80.194 0.000
.bf05_58_1 2.785 0.052 53.503 0.000
cnsc_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_03_t1 0.893 0.062 14.367 0.000
.bf05_08_t1 1.059 0.065 16.236 0.000
.bf05_13_t1 0.946 0.056 16.991 0.000
.bf05_18_t1 0.677 0.046 14.631 0.000
.bf05_23_t1 1.118 0.070 16.082 0.000
.bf05_28_t1 1.113 0.068 16.428 0.000
.bf05_33_t1 0.692 0.047 14.696 0.000
.bf05_38_t1 0.614 0.039 15.741 0.000
.bf05_43_t1 0.628 0.038 16.656 0.000
.bf05_48_t1 0.882 0.056 15.856 0.000
.bf05_53_t1 0.844 0.052 16.251 0.000
.bf05_58_t1 1.194 0.072 16.589 0.000
consc_curr1 1.000 tidy(fit_cfa_consc_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading))# A tibble: 12 × 11
term op label estimate std.error statistic p.value std.lv std.all std.nox
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 cons… =~ lamb… 1.10 0.0522 21.2 0 1.10 0.759 0.759
2 cons… =~ lamb… -0.907 0.0445 -20.4 0 -0.907 -0.741 -0.741
3 cons… =~ lamb… -0.897 0.0447 -20.1 0 -0.897 -0.733 -0.733
4 cons… =~ lamb… 0.808 0.0515 15.7 0 0.808 0.607 0.607
5 cons… =~ lamb… 0.783 0.0466 16.8 0 0.783 0.640 0.640
6 cons… =~ lamb… 0.755 0.0496 15.2 0 0.755 0.592 0.592
7 cons… =~ lamb… 0.704 0.0500 14.1 0 0.704 0.555 0.555
8 cons… =~ lamb… 0.675 0.0511 13.2 0 0.675 0.526 0.526
9 cons… =~ lamb… -0.664 0.0391 -17.0 0 -0.664 -0.646 -0.646
10 cons… =~ lamb… -0.655 0.0441 -14.9 0 -0.655 -0.581 -0.581
11 cons… =~ lamb… -0.459 0.0368 -12.5 0 -0.459 -0.501 -0.501
12 cons… =~ lamb… -0.435 0.0438 -9.92 0 -0.435 -0.408 -0.408
# ℹ 1 more variable: abs_loading <dbl>Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf05_03_t1_r = bf05_03_t1,
bf05_23_t1_r = bf05_23_t1,
bf05_48_t1_r = bf05_48_t1,
bf05_08_t1_r = bf05_08_t1,
bf05_28_t1_r = bf05_28_t1,
bf05_58_t1_r = bf05_58_t1,
bf05_03_t2_r = bf05_03_t2,
bf05_23_t2_r = bf05_23_t2,
bf05_48_t2_r = bf05_48_t2,
bf05_08_t2_r = bf05_08_t2,
bf05_28_t2_r = bf05_28_t2,
bf05_58_t2_r = bf05_58_t2) %>%
mutate(across(c(bf05_03_t1_r, bf05_23_t1_r, bf05_48_t1_r, bf05_08_t1_r, bf05_28_t1_r, bf05_58_t1_r,
bf05_03_t2_r, bf05_23_t2_r, bf05_48_t2_r, bf05_08_t2_r, bf05_28_t2_r, bf05_58_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(consc_curr_par1_t1 = rowMeans(across(c(bf05_03_t1_r, bf05_08_t1_r, bf05_38_t1, bf05_13_t1)), na.rm=T),
consc_curr_par2_t1 = rowMeans(across(c(bf05_18_t1, bf05_48_t1_r, bf05_58_t1_r, bf05_43_t1)), na.rm=T),
consc_curr_par3_t1 = rowMeans(across(c(bf05_33_t1, bf05_23_t1_r, bf05_28_t1_r, bf05_53_t1)), na.rm=T),
consc_curr_par1_t2 = rowMeans(across(c(bf05_03_t2_r, bf05_08_t2_r, bf05_38_t2, bf05_13_t2)), na.rm=T),
consc_curr_par2_t2 = rowMeans(across(c(bf05_18_t2, bf05_48_t2_r, bf05_58_t2_r, bf05_43_t2)), na.rm=T),
consc_curr_par3_t2 = rowMeans(across(c(bf05_33_t2, bf05_23_t2_r, bf05_28_t2_r, bf05_53_t2)), na.rm=T))Check CFA item loadings at T1
cfa_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*bf06_03_t1 + lambda1*bf06_03_t1 + lambda2*bf06_08_t1 + lambda3*bf06_13_t1 + lambda4*bf06_18_t1 + lambda5*bf06_23_t1 + lambda6*bf06_28_t1 + lambda7*bf06_33_t1 + lambda8*bf06_38_t1 + lambda9*bf06_43_t1 + lambda10*bf06_48_t1 + lambda11*bf06_53_t1 + lambda12*bf06_58_t1
# Intercepts
bf06_03_t1 ~ i1*1
bf06_08_t1 ~ 1
bf06_13_t1 ~ 1
bf06_18_t1 ~ 1
bf06_23_t1 ~ 1
bf06_28_t1 ~ 1
bf06_33_t1 ~ 1
bf06_38_t1 ~ 1
bf06_43_t1 ~ 1
bf06_48_t1 ~ 1
bf06_53_t1 ~ 1
bf06_58_t1 ~ 1
# Unique Variances
bf06_03_t1 ~~ bf06_03_t1
bf06_08_t1 ~~ bf06_08_t1
bf06_13_t1 ~~ bf06_13_t1
bf06_18_t1 ~~ bf06_18_t1
bf06_23_t1 ~~ bf06_23_t1
bf06_28_t1 ~~ bf06_28_t1
bf06_33_t1 ~~ bf06_33_t1
bf06_38_t1 ~~ bf06_38_t1
bf06_43_t1 ~~ bf06_43_t1
bf06_48_t1 ~~ bf06_48_t1
bf06_53_t1 ~~ bf06_53_t1
bf06_58_t1 ~~ bf06_58_t1
# Latent Variable Means
consc_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
'
fit_cfa_consc_ideal <- cfa(cfa_consc_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_cfa_consc_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 22 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 609 619
Number of missing patterns 1
Model Test User Model:
Test statistic 187.648
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1314.476
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.893
Tucker-Lewis Index (TLI) 0.869
Robust Comparative Fit Index (CFI) 0.893
Robust Tucker-Lewis Index (TLI) 0.869
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -8614.974
Loglikelihood unrestricted model (H1) -8521.150
Akaike (AIC) 17301.949
Bayesian (BIC) 17460.774
Sample-size adjusted Bayesian (SABIC) 17346.482
Root Mean Square Error of Approximation:
RMSEA 0.064
90 Percent confidence interval - lower 0.054
90 Percent confidence interval - upper 0.074
P-value H_0: RMSEA <= 0.050 0.011
P-value H_0: RMSEA >= 0.080 0.004
Robust RMSEA 0.064
90 Percent confidence interval - lower 0.054
90 Percent confidence interval - upper 0.074
P-value H_0: Robust RMSEA <= 0.050 0.011
P-value H_0: Robust RMSEA >= 0.080 0.004
Standardized Root Mean Square Residual:
SRMR 0.045
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
consc_ideal1 =~
b06_03_ (lmb1) 0.370 0.050 7.373 0.000
b06_08_ (lmb2) 0.387 0.040 9.588 0.000
b06_13_ (lmb3) -0.381 0.045 -8.386 0.000
b06_18_ (lmb4) -0.375 0.034 -11.085 0.000
b06_23_ (lmb5) 0.505 0.037 13.464 0.000
b06_28_ (lmb6) 0.533 0.046 11.630 0.000
b06_33_ (lmb7) -0.475 0.033 -14.577 0.000
b06_38_ (lmb8) -0.350 0.029 -12.147 0.000
b06_43_ (lmb9) -0.339 0.028 -12.075 0.000
b06_48_ (lm10) 0.402 0.030 13.292 0.000
b06_53_ (lm11) -0.401 0.028 -14.246 0.000
b06_58_ (lm12) 0.468 0.039 11.940 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_03_1 (i1) 1.695 0.046 36.829 0.000
.bf06_08_1 1.629 0.038 43.084 0.000
.bf06_13_1 4.258 0.042 101.045 0.000
.bf06_18_1 4.348 0.032 135.448 0.000
.bf06_23_1 1.522 0.037 41.626 0.000
.bf06_28_1 1.959 0.044 44.648 0.000
.bf06_33_1 4.407 0.032 137.144 0.000
.bf06_38_1 4.635 0.028 167.198 0.000
.bf06_43_1 4.571 0.027 170.332 0.000
.bf06_48_1 1.374 0.029 46.598 0.000
.bf06_53_1 4.581 0.028 165.490 0.000
.bf06_58_1 1.749 0.038 46.500 0.000
consc_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_03_t1 1.153 0.068 16.920 0.000
.bf06_08_t1 0.720 0.044 16.526 0.000
.bf06_13_t1 0.936 0.056 16.776 0.000
.bf06_18_t1 0.487 0.030 16.149 0.000
.bf06_23_t1 0.560 0.036 15.453 0.000
.bf06_28_t1 0.888 0.055 16.062 0.000
.bf06_33_t1 0.404 0.027 14.974 0.000
.bf06_38_t1 0.345 0.022 15.902 0.000
.bf06_43_t1 0.324 0.020 15.832 0.000
.bf06_48_t1 0.368 0.024 15.566 0.000
.bf06_53_t1 0.306 0.020 15.092 0.000
.bf06_58_t1 0.642 0.040 15.968 0.000
consc_ideal1 1.000 tidy(fit_cfa_consc_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading))# A tibble: 12 × 11
term op label estimate std.error statistic p.value std.lv std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 consc_ideal… =~ lamb… 0.533 0.0458 11.6 0 0.533 0.492
2 consc_ideal… =~ lamb… 0.505 0.0375 13.5 0 0.505 0.559
3 consc_ideal… =~ lamb… -0.475 0.0326 -14.6 0 -0.475 -0.599
4 consc_ideal… =~ lamb… 0.468 0.0392 11.9 0 0.468 0.504
5 consc_ideal… =~ lamb… 0.402 0.0302 13.3 0 0.402 0.552
6 consc_ideal… =~ lamb… -0.401 0.0282 -14.2 0 -0.401 -0.588
7 consc_ideal… =~ lamb… 0.387 0.0404 9.59 0 0.387 0.415
8 consc_ideal… =~ lamb… -0.381 0.0454 -8.39 0 -0.381 -0.366
9 consc_ideal… =~ lamb… -0.375 0.0339 -11.1 0 -0.375 -0.474
10 consc_ideal… =~ lamb… 0.370 0.0501 7.37 1.67e-13 0.370 0.325
11 consc_ideal… =~ lamb… -0.350 0.0288 -12.1 0 -0.350 -0.512
12 consc_ideal… =~ lamb… -0.339 0.0281 -12.1 0 -0.339 -0.512
# ℹ 2 more variables: std.nox <dbl>, abs_loading <dbl>Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf06_03_t1_r = bf06_03_t1,
bf06_23_t1_r = bf06_23_t1,
bf06_48_t1_r = bf06_48_t1,
bf06_08_t1_r = bf06_08_t1,
bf06_28_t1_r = bf06_28_t1,
bf06_58_t1_r = bf06_58_t1,
bf06_03_t2_r = bf06_03_t2,
bf06_23_t2_r = bf06_23_t2,
bf06_48_t2_r = bf06_48_t2,
bf06_08_t2_r = bf06_08_t2,
bf06_28_t2_r = bf06_28_t2,
bf06_58_t2_r = bf06_58_t2) %>%
mutate(across(c(bf06_03_t1_r, bf06_23_t1_r, bf06_48_t1_r, bf06_08_t1_r, bf06_28_t1_r, bf06_58_t1_r,
bf06_03_t2_r, bf06_23_t2_r, bf06_48_t2_r, bf06_08_t2_r, bf06_28_t2_r, bf06_58_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(consc_ideal_par1_t1 = rowMeans(across(c(bf06_28_t1_r, bf06_53_t1, bf06_18_t1, bf06_43_t1)), na.rm=T),
consc_ideal_par2_t1 = rowMeans(across(c(bf06_23_t1_r, bf06_48_t1_r, bf06_13_t1, bf06_38_t1)), na.rm=T),
consc_ideal_par3_t1 = rowMeans(across(c(bf06_33_t1, bf06_58_t1_r, bf06_08_t1_r, bf06_03_t1_r)), na.rm=T),
consc_ideal_par1_t2 = rowMeans(across(c(bf06_28_t2_r, bf06_53_t2, bf06_18_t2, bf06_43_t2)), na.rm=T),
consc_ideal_par2_t2 = rowMeans(across(c(bf06_23_t2_r, bf06_48_t2_r, bf06_13_t2, bf06_38_t2)), na.rm=T),
consc_ideal_par3_t2 = rowMeans(across(c(bf06_33_t2, bf06_58_t2_r, bf06_08_t2_r, bf06_03_t2_r)), na.rm=T))Check CFA item loadings at T1
cfa_neuro_curr <- '
#| warning: false
# Define the latent factors
neuro_curr1 =~ NA*bf05_04_t1 + lambda1*bf05_04_t1 + lambda2*bf05_09_t1 + lambda3*bf05_14_t1 + lambda4*bf05_19_t1 + lambda5*bf05_24_t1 + lambda6*bf05_29_t1 + lambda7*bf05_34_t1 + lambda8*bf05_39_t1 + lambda9*bf05_44_t1 + lambda10*bf05_49_t1 + lambda11*bf05_54_t1 + lambda12*bf05_59_t1
# Intercepts
bf05_04_t1 ~ i1*1
bf05_09_t1 ~ 1
bf05_14_t1 ~ 1
bf05_19_t1 ~ 1
bf05_24_t1 ~ 1
bf05_29_t1 ~ 1
bf05_34_t1 ~ 1
bf05_39_t1 ~ 1
bf05_44_t1 ~ 1
bf05_49_t1 ~ 1
bf05_54_t1 ~ 1
bf05_59_t1 ~ 1
# Unique Variances
bf05_04_t1 ~~ bf05_04_t1
bf05_09_t1 ~~ bf05_09_t1
bf05_14_t1 ~~ bf05_14_t1
bf05_19_t1 ~~ bf05_19_t1
bf05_24_t1 ~~ bf05_24_t1
bf05_29_t1 ~~ bf05_29_t1
bf05_34_t1 ~~ bf05_34_t1
bf05_39_t1 ~~ bf05_39_t1
bf05_44_t1 ~~ bf05_44_t1
bf05_49_t1 ~~ bf05_49_t1
bf05_54_t1 ~~ bf05_54_t1
bf05_59_t1 ~~ bf05_59_t1
# Latent Variable Means
neuro_curr1 ~ 0*1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
'
fit_cfa_neuro_curr <- cfa(cfa_neuro_curr, data = df_sbsa_wide_pers, mimic = "mplus")Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
16 39 87 287 339 340 366 391 527 609summary(fit_cfa_neuro_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 34 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 609 619
Number of missing patterns 3
Model Test User Model:
Test statistic 569.140
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 3227.954
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.837
Tucker-Lewis Index (TLI) 0.801
Robust Comparative Fit Index (CFI) 0.837
Robust Tucker-Lewis Index (TLI) 0.801
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -10673.638
Loglikelihood unrestricted model (H1) -10389.067
Akaike (AIC) 21419.275
Bayesian (BIC) 21578.101
Sample-size adjusted Bayesian (SABIC) 21463.809
Root Mean Square Error of Approximation:
RMSEA 0.125
90 Percent confidence interval - lower 0.116
90 Percent confidence interval - upper 0.135
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.125
90 Percent confidence interval - lower 0.116
90 Percent confidence interval - upper 0.135
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.059
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
neuro_curr1 =~
b05_04_ (lmb1) 0.888 0.047 18.818 0.000
b05_09_ (lmb2) 0.760 0.048 15.751 0.000
b05_14_ (lmb3) -0.855 0.051 -16.904 0.000
b05_19_ (lmb4) -0.612 0.043 -14.319 0.000
b05_24_ (lmb5) 0.800 0.048 16.784 0.000
b05_29_ (lmb6) 0.930 0.045 20.642 0.000
b05_34_ (lmb7) -0.807 0.044 -18.345 0.000
b05_39_ (lmb8) -0.922 0.048 -19.389 0.000
b05_44_ (lmb9) 0.794 0.046 17.206 0.000
b05_49_ (lm10) 0.586 0.049 11.905 0.000
b05_54_ (lm11) -0.957 0.048 -20.023 0.000
b05_59_ (lm12) -0.790 0.052 -15.257 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_04_1 (i1) 2.703 0.052 52.176 0.000
.bf05_09_1 2.871 0.051 56.336 0.000
.bf05_14_1 3.256 0.054 60.173 0.000
.bf05_19_1 3.658 0.044 82.408 0.000
.bf05_24_1 2.906 0.051 57.018 0.000
.bf05_29_1 2.865 0.051 56.646 0.000
.bf05_34_1 3.885 0.048 80.965 0.000
.bf05_39_1 3.404 0.052 65.195 0.000
.bf05_44_1 3.112 0.049 63.152 0.000
.bf05_49_1 2.355 0.050 47.242 0.000
.bf05_54_1 3.337 0.053 62.949 0.000
.bf05_59_1 3.171 0.054 58.589 0.000
neur_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_04_t1 0.846 0.054 15.600 0.000
.bf05_09_t1 1.002 0.061 16.301 0.000
.bf05_14_t1 1.053 0.066 16.036 0.000
.bf05_19_t1 0.826 0.050 16.567 0.000
.bf05_24_t1 0.943 0.059 16.115 0.000
.bf05_29_t1 0.694 0.047 14.800 0.000
.bf05_34_t1 0.751 0.048 15.706 0.000
.bf05_39_t1 0.807 0.054 14.922 0.000
.bf05_44_t1 0.849 0.054 15.733 0.000
.bf05_49_t1 1.169 0.069 16.884 0.000
.bf05_54_t1 0.795 0.054 14.757 0.000
.bf05_59_t1 1.160 0.071 16.276 0.000
neuro_curr1 1.000 tidy(fit_cfa_neuro_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading))# A tibble: 12 × 11
term op label estimate std.error statistic p.value std.lv std.all std.nox
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 neur… =~ lamb… -0.957 0.0478 -20.0 0 -0.957 -0.732 -0.732
2 neur… =~ lamb… 0.930 0.0450 20.6 0 0.930 0.745 0.745
3 neur… =~ lamb… -0.922 0.0476 -19.4 0 -0.922 -0.716 -0.716
4 neur… =~ lamb… 0.888 0.0472 18.8 0 0.888 0.694 0.694
5 neur… =~ lamb… -0.855 0.0506 -16.9 0 -0.855 -0.640 -0.640
6 neur… =~ lamb… -0.807 0.0440 -18.3 0 -0.807 -0.681 -0.681
7 neur… =~ lamb… 0.800 0.0476 16.8 0 0.800 0.636 0.636
8 neur… =~ lamb… 0.794 0.0461 17.2 0 0.794 0.653 0.653
9 neur… =~ lamb… -0.790 0.0518 -15.3 0 -0.790 -0.592 -0.592
10 neur… =~ lamb… 0.760 0.0482 15.8 0 0.760 0.604 0.604
11 neur… =~ lamb… -0.612 0.0427 -14.3 0 -0.612 -0.559 -0.559
12 neur… =~ lamb… 0.586 0.0492 11.9 0 0.586 0.477 0.477
# ℹ 1 more variable: abs_loading <dbl>Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf05_29_t1_r = bf05_29_t1,
bf05_04_t1_r = bf05_04_t1,
bf05_24_t1_r = bf05_24_t1,
bf05_44_t1_r = bf05_44_t1,
bf05_09_t1_r = bf05_09_t1,
bf05_49_t1_r = bf05_49_t1,
bf05_29_t2_r = bf05_29_t2,
bf05_04_t2_r = bf05_04_t2,
bf05_24_t2_r = bf05_24_t2,
bf05_44_t2_r = bf05_44_t2,
bf05_09_t2_r = bf05_09_t2,
bf05_49_t2_r = bf05_49_t2) %>%
mutate(across(c(bf05_29_t1_r, bf05_04_t1_r, bf05_24_t1_r, bf05_44_t1_r, bf05_09_t1_r, bf05_49_t1_r,
bf05_29_t2_r, bf05_04_t2_r, bf05_24_t2_r, bf05_44_t2_r, bf05_09_t2_r, bf05_49_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(neuro_curr_par1_t1 = rowMeans(across(c(bf05_54_t1, bf05_34_t1, bf05_59_t1, bf05_49_t1_r)), na.rm=T),
neuro_curr_par2_t1 = rowMeans(across(c(bf05_29_t1_r, bf05_14_t1, bf05_44_t1_r, bf05_19_t1)), na.rm=T),
neuro_curr_par3_t1 = rowMeans(across(c(bf05_39_t1, bf05_04_t1_r, bf05_24_t1_r, bf05_09_t1_r)), na.rm=T),
neuro_curr_par1_t2 = rowMeans(across(c(bf05_54_t2, bf05_34_t2, bf05_59_t2, bf05_49_t2_r)), na.rm=T),
neuro_curr_par2_t2 = rowMeans(across(c(bf05_29_t2_r, bf05_14_t2, bf05_44_t2_r, bf05_19_t2)), na.rm=T),
neuro_curr_par3_t2 = rowMeans(across(c(bf05_39_t2, bf05_04_t2_r, bf05_24_t2_r, bf05_09_t2_r)), na.rm=T))Check CFA item loadings at T1
cfa_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*bf06_04_t1 + lambda1*bf06_04_t1 + lambda2*bf06_09_t1 + lambda3*bf06_14_t1 + lambda4*bf06_19_t1 + lambda5*bf06_24_t1 + lambda6*bf06_29_t1 + lambda7*bf06_34_t1 + lambda8*bf06_39_t1 + lambda9*bf06_44_t1 + lambda10*bf06_49_t1 + lambda11*bf06_54_t1 + lambda12*bf06_59_t1
# Intercepts
bf06_04_t1 ~ i1*1
bf06_09_t1 ~ 1
bf06_14_t1 ~ 1
bf06_19_t1 ~ 1
bf06_24_t1 ~ 1
bf06_29_t1 ~ 1
bf06_34_t1 ~ 1
bf06_39_t1 ~ 1
bf06_44_t1 ~ 1
bf06_49_t1 ~ 1
bf06_54_t1 ~ 1
bf06_59_t1 ~ 1
# Unique Variances
bf06_04_t1 ~~ bf06_04_t1
bf06_09_t1 ~~ bf06_09_t1
bf06_14_t1 ~~ bf06_14_t1
bf06_19_t1 ~~ bf06_19_t1
bf06_24_t1 ~~ bf06_24_t1
bf06_29_t1 ~~ bf06_29_t1
bf06_34_t1 ~~ bf06_34_t1
bf06_39_t1 ~~ bf06_39_t1
bf06_44_t1 ~~ bf06_44_t1
bf06_49_t1 ~~ bf06_49_t1
bf06_54_t1 ~~ bf06_54_t1
bf06_59_t1 ~~ bf06_59_t1
# Latent Variable Means
neuro_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
'
fit_cfa_neuro_ideal <- cfa(cfa_neuro_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_cfa_neuro_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 41 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 609 619
Number of missing patterns 3
Model Test User Model:
Test statistic 130.619
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1100.025
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.926
Tucker-Lewis Index (TLI) 0.909
Robust Comparative Fit Index (CFI) 0.926
Robust Tucker-Lewis Index (TLI) 0.909
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -9102.265
Loglikelihood unrestricted model (H1) -9036.955
Akaike (AIC) 18276.529
Bayesian (BIC) 18435.355
Sample-size adjusted Bayesian (SABIC) 18321.063
Root Mean Square Error of Approximation:
RMSEA 0.048
90 Percent confidence interval - lower 0.038
90 Percent confidence interval - upper 0.059
P-value H_0: RMSEA <= 0.050 0.590
P-value H_0: RMSEA >= 0.080 0.000
Robust RMSEA 0.048
90 Percent confidence interval - lower 0.038
90 Percent confidence interval - upper 0.059
P-value H_0: Robust RMSEA <= 0.050 0.589
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.040
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
neuro_ideal1 =~
b06_04_ (lmb1) 0.302 0.031 9.777 0.000
b06_09_ (lmb2) 0.292 0.035 8.319 0.000
b06_14_ (lmb3) -0.451 0.039 -11.560 0.000
b06_19_ (lmb4) -0.509 0.044 -11.705 0.000
b06_24_ (lmb5) 0.288 0.031 9.189 0.000
b06_29_ (lmb6) 0.376 0.046 8.207 0.000
b06_34_ (lmb7) -0.562 0.041 -13.631 0.000
b06_39_ (lmb8) -0.532 0.036 -14.861 0.000
b06_44_ (lmb9) 0.364 0.034 10.618 0.000
b06_49_ (lm10) 0.385 0.067 5.771 0.000
b06_54_ (lm11) -0.462 0.032 -14.294 0.000
b06_59_ (lm12) -0.467 0.040 -11.627 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_04_1 (i1) 4.571 0.029 159.374 0.000
.bf06_09_1 4.383 0.032 136.584 0.000
.bf06_14_1 1.708 0.037 46.235 0.000
.bf06_19_1 2.141 0.041 51.959 0.000
.bf06_24_1 4.642 0.029 160.572 0.000
.bf06_29_1 4.217 0.042 100.634 0.000
.bf06_34_1 1.898 0.040 47.604 0.000
.bf06_39_1 1.651 0.035 47.110 0.000
.bf06_44_1 4.396 0.032 137.034 0.000
.bf06_49_1 3.537 0.060 58.812 0.000
.bf06_54_1 1.496 0.031 47.603 0.000
.bf06_59_1 1.980 0.038 52.200 0.000
neuro_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_04_t1 0.410 0.025 16.354 0.000
.bf06_09_t1 0.542 0.033 16.667 0.000
.bf06_14_t1 0.627 0.039 15.891 0.000
.bf06_19_t1 0.775 0.049 15.827 0.000
.bf06_24_t1 0.426 0.026 16.475 0.000
.bf06_29_t1 0.926 0.056 16.673 0.000
.bf06_34_t1 0.653 0.043 15.118 0.000
.bf06_39_t1 0.464 0.032 14.435 0.000
.bf06_44_t1 0.494 0.031 16.145 0.000
.bf06_49_t1 2.054 0.120 17.104 0.000
.bf06_54_t1 0.388 0.026 14.738 0.000
.bf06_59_t1 0.659 0.042 15.840 0.000
neuro_ideal1 1.000 tidy(fit_cfa_neuro_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading))# A tibble: 12 × 11
term op label estimate std.error statistic p.value std.lv std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 neuro_ideal… =~ lamb… -0.562 0.0412 -13.6 0 -0.562 -0.571
2 neuro_ideal… =~ lamb… -0.532 0.0358 -14.9 0 -0.532 -0.616
3 neuro_ideal… =~ lamb… -0.509 0.0435 -11.7 0 -0.509 -0.501
4 neuro_ideal… =~ lamb… -0.467 0.0401 -11.6 0 -0.467 -0.498
5 neuro_ideal… =~ lamb… -0.462 0.0323 -14.3 0 -0.462 -0.596
6 neuro_ideal… =~ lamb… -0.451 0.0390 -11.6 0 -0.451 -0.495
7 neuro_ideal… =~ lamb… 0.385 0.0667 5.77 7.87e- 9 0.385 0.259
8 neuro_ideal… =~ lamb… 0.376 0.0459 8.21 2.22e-16 0.376 0.364
9 neuro_ideal… =~ lamb… 0.364 0.0343 10.6 0 0.364 0.460
10 neuro_ideal… =~ lamb… 0.302 0.0309 9.78 0 0.302 0.427
11 neuro_ideal… =~ lamb… 0.292 0.0351 8.32 0 0.292 0.369
12 neuro_ideal… =~ lamb… 0.288 0.0314 9.19 0 0.288 0.404
# ℹ 2 more variables: std.nox <dbl>, abs_loading <dbl>Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf06_29_t1_r = bf06_29_t1,
bf06_04_t1_r = bf06_04_t1,
bf06_24_t1_r = bf06_24_t1,
bf06_44_t1_r = bf06_44_t1,
bf06_09_t1_r = bf06_09_t1,
bf06_49_t1_r = bf06_49_t1,
bf06_29_t2_r = bf06_29_t2,
bf06_04_t2_r = bf06_04_t2,
bf06_24_t2_r = bf06_24_t2,
bf06_44_t2_r = bf06_44_t2,
bf06_09_t2_r = bf06_09_t2,
bf06_49_t2_r = bf06_49_t2) %>%
mutate(across(c(bf06_29_t1_r, bf06_04_t1_r, bf06_24_t1_r, bf06_44_t1_r, bf06_09_t1_r, bf06_49_t1_r,
bf06_29_t2_r, bf06_04_t2_r, bf06_24_t2_r, bf06_44_t2_r, bf06_09_t2_r, bf06_49_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(neuro_ideal_par1_t1 = rowMeans(across(c(bf06_34_t1, bf06_14_t1, bf06_44_t1_r, bf06_24_t1_r)), na.rm=T),
neuro_ideal_par2_t1 = rowMeans(across(c(bf06_39_t1, bf06_54_t1, bf06_29_t1_r, bf06_09_t1_r)), na.rm=T),
neuro_ideal_par3_t1 = rowMeans(across(c(bf06_19_t1, bf06_59_t1, bf06_49_t1_r, bf06_04_t1_r)), na.rm=T),
neuro_ideal_par1_t2 = rowMeans(across(c(bf06_34_t2, bf06_14_t2, bf06_44_t2_r, bf06_24_t2_r)), na.rm=T),
neuro_ideal_par2_t2 = rowMeans(across(c(bf06_39_t2, bf06_54_t2, bf06_29_t2_r, bf06_09_t2_r)), na.rm=T),
neuro_ideal_par3_t2 = rowMeans(across(c(bf06_19_t2, bf06_59_t2, bf06_49_t2_r, bf06_04_t2_r)), na.rm=T))Check CFA item loadings at T1
cfa_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*bf05_05_t1 + lambda1*bf05_05_t1 + lambda2*bf05_10_t1 + lambda3*bf05_15_t1 + lambda4*bf05_20_t1 + lambda5*bf05_25_t1 + lambda6*bf05_30_t1 + lambda7*bf05_35_t1 + lambda8*bf05_40_t1 + lambda9*bf05_45_t1 + lambda10*bf05_50_t1 + lambda11*bf05_55_t1 + lambda12*bf05_60_t1
# Intercepts
bf05_05_t1 ~ i1*1
bf05_10_t1 ~ 1
bf05_15_t1 ~ 1
bf05_20_t1 ~ 1
bf05_25_t1 ~ 1
bf05_30_t1 ~ 1
bf05_35_t1 ~ 1
bf05_40_t1 ~ 1
bf05_45_t1 ~ 1
bf05_50_t1 ~ 1
bf05_55_t1 ~ 1
bf05_60_t1 ~ 1
# Unique Variances
bf05_05_t1 ~~ bf05_05_t1
bf05_10_t1 ~~ bf05_10_t1
bf05_15_t1 ~~ bf05_15_t1
bf05_20_t1 ~~ bf05_20_t1
bf05_25_t1 ~~ bf05_25_t1
bf05_30_t1 ~~ bf05_30_t1
bf05_35_t1 ~~ bf05_35_t1
bf05_40_t1 ~~ bf05_40_t1
bf05_45_t1 ~~ bf05_45_t1
bf05_50_t1 ~~ bf05_50_t1
bf05_55_t1 ~~ bf05_55_t1
bf05_60_t1 ~~ bf05_60_t1
# Latent Variable Means
openn_curr1 ~ 0*1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
'
fit_cfa_openn_curr <- cfa(cfa_openn_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_cfa_openn_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 46 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 609 619
Number of missing patterns 4
Model Test User Model:
Test statistic 642.446
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1745.061
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.650
Tucker-Lewis Index (TLI) 0.572
Robust Comparative Fit Index (CFI) 0.649
Robust Tucker-Lewis Index (TLI) 0.571
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -10776.912
Loglikelihood unrestricted model (H1) -10455.689
Akaike (AIC) 21625.824
Bayesian (BIC) 21784.649
Sample-size adjusted Bayesian (SABIC) 21670.357
Root Mean Square Error of Approximation:
RMSEA 0.134
90 Percent confidence interval - lower 0.125
90 Percent confidence interval - upper 0.143
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.134
90 Percent confidence interval - lower 0.125
90 Percent confidence interval - upper 0.143
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.084
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
openn_curr1 =~
b05_05_ (lmb1) 0.010 0.059 0.173 0.863
b05_10_ (lmb2) -0.498 0.042 -11.728 0.000
b05_15_ (lmb3) -0.455 0.049 -9.322 0.000
b05_20_ (lmb4) -0.792 0.053 -14.965 0.000
b05_25_ (lmb5) 0.506 0.056 8.980 0.000
b05_30_ (lmb6) 0.568 0.056 10.148 0.000
b05_35_ (lmb7) -0.711 0.046 -15.283 0.000
b05_40_ (lmb8) -0.416 0.044 -9.473 0.000
b05_45_ (lmb9) 0.485 0.050 9.612 0.000
b05_50_ (lm10) 0.698 0.059 11.857 0.000
b05_55_ (lm11) 0.586 0.049 11.922 0.000
b05_60_ (lm12) -0.555 0.050 -11.191 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_05_1 (i1) 3.012 0.053 56.496 0.000
.bf05_10_1 4.080 0.039 104.539 0.000
.bf05_15_1 3.573 0.041 86.216 0.000
.bf05_20_1 3.811 0.048 80.116 0.000
.bf05_25_1 2.374 0.052 46.035 0.000
.bf05_30_1 2.610 0.052 50.370 0.000
.bf05_35_1 3.890 0.042 92.239 0.000
.bf05_40_1 3.901 0.040 96.671 0.000
.bf05_45_1 2.056 0.047 44.152 0.000
.bf05_50_1 2.512 0.054 46.339 0.000
.bf05_55_1 2.489 0.047 53.065 0.000
.bf05_60_1 3.521 0.043 81.036 0.000
opnn_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_05_t1 1.725 0.099 17.421 0.000
.bf05_10_t1 0.680 0.045 15.214 0.000
.bf05_15_t1 0.839 0.055 15.303 0.000
.bf05_20_t1 0.751 0.067 11.193 0.000
.bf05_25_t1 1.364 0.083 16.472 0.000
.bf05_30_t1 1.308 0.081 16.182 0.000
.bf05_35_t1 0.578 0.052 11.111 0.000
.bf05_40_t1 0.819 0.050 16.340 0.000
.bf05_45_t1 1.085 0.066 16.344 0.000
.bf05_50_t1 1.303 0.086 15.165 0.000
.bf05_55_t1 0.997 0.063 15.869 0.000
.bf05_60_t1 0.841 0.058 14.550 0.000
openn_curr1 1.000 tidy(fit_cfa_openn_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading))# A tibble: 12 × 11
term op label estimate std.error statistic p.value std.lv std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 openn_curr… =~ lamb… -0.792 0.0529 -15.0 0 -0.792 -0.675
2 openn_curr… =~ lamb… -0.711 0.0465 -15.3 0 -0.711 -0.683
3 openn_curr… =~ lamb… 0.698 0.0589 11.9 0 0.698 0.522
4 openn_curr… =~ lamb… 0.586 0.0492 11.9 0 0.586 0.506
5 openn_curr… =~ lamb… 0.568 0.0559 10.1 0 0.568 0.445
6 openn_curr… =~ lamb… -0.555 0.0496 -11.2 0 -0.555 -0.518
7 openn_curr… =~ lamb… 0.506 0.0564 8.98 0 0.506 0.398
8 openn_curr… =~ lamb… -0.498 0.0425 -11.7 0 -0.498 -0.517
9 openn_curr… =~ lamb… 0.485 0.0505 9.61 0 0.485 0.422
10 openn_curr… =~ lamb… -0.455 0.0488 -9.32 0 -0.455 -0.445
11 openn_curr… =~ lamb… -0.416 0.0440 -9.47 0 -0.416 -0.418
12 openn_curr… =~ lamb… 0.0103 0.0593 0.173 0.863 0.0103 0.00781
# ℹ 2 more variables: std.nox <dbl>, abs_loading <dbl>Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf05_50_t1_r = bf05_50_t1,
bf05_55_t1_r = bf05_55_t1,
bf05_30_t1_r = bf05_30_t1,
bf05_25_t1_r = bf05_25_t1,
bf05_45_t1_r = bf05_45_t1,
bf05_05_t1_r = bf05_05_t1,
bf05_50_t2_r = bf05_50_t2,
bf05_55_t2_r = bf05_55_t2,
bf05_30_t2_r = bf05_30_t2,
bf05_25_t2_r = bf05_25_t2,
bf05_45_t2_r = bf05_45_t2,
bf05_05_t2_r = bf05_05_t2) %>%
mutate(across(c(bf05_50_t1_r, bf05_55_t1_r, bf05_30_t1_r, bf05_25_t1_r, bf05_45_t1_r, bf05_05_t1_r,
bf05_50_t2_r, bf05_55_t2_r, bf05_30_t2_r, bf05_25_t2_r, bf05_45_t2_r, bf05_05_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(openn_curr_par1_t1 = rowMeans(across(c(bf05_20_t1, bf05_60_t1, bf05_45_t1_r, bf05_05_t1_r)), na.rm=T),
openn_curr_par2_t1 = rowMeans(across(c(bf05_35_t1, bf05_30_t1_r, bf05_10_t1, bf05_40_t1)), na.rm=T),
openn_curr_par3_t1 = rowMeans(across(c(bf05_50_t1_r, bf05_55_t1_r, bf05_25_t1_r, bf05_15_t1)), na.rm=T),
openn_curr_par1_t2 = rowMeans(across(c(bf05_20_t2, bf05_60_t2, bf05_45_t2_r, bf05_05_t2_r)), na.rm=T),
openn_curr_par2_t2 = rowMeans(across(c(bf05_35_t2, bf05_30_t2_r, bf05_10_t2, bf05_40_t2)), na.rm=T),
openn_curr_par3_t2 = rowMeans(across(c(bf05_50_t2_r, bf05_55_t2_r, bf05_25_t2_r, bf05_15_t2)), na.rm=T))Check CFA item loadings at T1
cfa_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*bf06_05_t1 + lambda1*bf06_05_t1 + lambda2*bf06_10_t1 + lambda3*bf06_15_t1 + lambda4*bf06_20_t1 + lambda5*bf06_25_t1 + lambda6*bf06_30_t1 + lambda7*bf06_35_t1 + lambda8*bf06_40_t1 + lambda9*bf06_45_t1 + lambda10*bf06_50_t1 + lambda11*bf06_55_t1 + lambda12*bf06_60_t1
# Intercepts
bf06_05_t1 ~ i1*1
bf06_10_t1 ~ 1
bf06_15_t1 ~ 1
bf06_20_t1 ~ 1
bf06_25_t1 ~ 1
bf06_30_t1 ~ 1
bf06_35_t1 ~ 1
bf06_40_t1 ~ 1
bf06_45_t1 ~ 1
bf06_50_t1 ~ 1
bf06_55_t1 ~ 1
bf06_60_t1 ~ 1
# Unique Variances
bf06_05_t1 ~~ bf06_05_t1
bf06_10_t1 ~~ bf06_10_t1
bf06_15_t1 ~~ bf06_15_t1
bf06_20_t1 ~~ bf06_20_t1
bf06_25_t1 ~~ bf06_25_t1
bf06_30_t1 ~~ bf06_30_t1
bf06_35_t1 ~~ bf06_35_t1
bf06_40_t1 ~~ bf06_40_t1
bf06_45_t1 ~~ bf06_45_t1
bf06_50_t1 ~~ bf06_50_t1
bf06_55_t1 ~~ bf06_55_t1
bf06_60_t1 ~~ bf06_60_t1
# Latent Variable Means
openn_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
'
fit_cfa_openn_ideal <- cfa(cfa_openn_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_cfa_openn_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 47 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 609 619
Number of missing patterns 2
Model Test User Model:
Test statistic 338.475
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1263.953
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.763
Tucker-Lewis Index (TLI) 0.710
Robust Comparative Fit Index (CFI) 0.763
Robust Tucker-Lewis Index (TLI) 0.710
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -9929.277
Loglikelihood unrestricted model (H1) -9760.040
Akaike (AIC) 19930.554
Bayesian (BIC) 20089.379
Sample-size adjusted Bayesian (SABIC) 19975.087
Root Mean Square Error of Approximation:
RMSEA 0.093
90 Percent confidence interval - lower 0.084
90 Percent confidence interval - upper 0.103
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.988
Robust RMSEA 0.093
90 Percent confidence interval - lower 0.084
90 Percent confidence interval - upper 0.103
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.988
Standardized Root Mean Square Residual:
SRMR 0.064
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
openn_ideal1 =~
b06_05_ (lmb1) 0.089 0.067 1.327 0.184
b06_10_ (lmb2) 0.355 0.033 10.849 0.000
b06_15_ (lmb3) 0.308 0.028 10.941 0.000
b06_20_ (lmb4) 0.663 0.040 16.479 0.000
b06_25_ (lmb5) -0.470 0.050 -9.403 0.000
b06_30_ (lmb6) -0.492 0.063 -7.857 0.000
b06_35_ (lmb7) 0.643 0.038 17.108 0.000
b06_40_ (lmb8) 0.319 0.047 6.829 0.000
b06_45_ (lmb9) -0.327 0.047 -6.928 0.000
b06_50_ (lm10) -0.594 0.050 -11.944 0.000
b06_55_ (lm11) -0.417 0.054 -7.764 0.000
b06_60_ (lm12) 0.356 0.031 11.477 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_05_1 (i1) 3.206 0.060 53.863 0.000
.bf06_10_1 4.463 0.030 147.530 0.000
.bf06_15_1 4.591 0.026 180.034 0.000
.bf06_20_1 4.276 0.039 109.103 0.000
.bf06_25_1 1.908 0.046 41.798 0.000
.bf06_30_1 2.036 0.057 35.611 0.000
.bf06_35_1 4.269 0.037 116.063 0.000
.bf06_40_1 3.959 0.042 93.286 0.000
.bf06_45_1 1.678 0.042 39.754 0.000
.bf06_50_1 2.079 0.047 44.151 0.000
.bf06_55_1 2.182 0.049 44.579 0.000
.bf06_60_1 4.527 0.029 157.658 0.000
openn_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_05_t1 2.146 0.123 17.418 0.000
.bf06_10_t1 0.432 0.027 15.862 0.000
.bf06_15_t1 0.301 0.020 15.442 0.000
.bf06_20_t1 0.495 0.040 12.414 0.000
.bf06_25_t1 1.048 0.064 16.306 0.000
.bf06_30_t1 1.746 0.104 16.754 0.000
.bf06_35_t1 0.411 0.035 11.792 0.000
.bf06_40_t1 0.995 0.059 16.965 0.000
.bf06_45_t1 0.979 0.058 16.855 0.000
.bf06_50_t1 0.997 0.064 15.699 0.000
.bf06_55_t1 1.286 0.077 16.792 0.000
.bf06_60_t1 0.375 0.024 15.507 0.000
openn_ideal1 1.000 tidy(fit_cfa_openn_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading))# A tibble: 12 × 11
term op label estimate std.error statistic p.value std.lv std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 openn_idea… =~ lamb… 0.663 0.0403 16.5 0 0.663 0.686
2 openn_idea… =~ lamb… 0.643 0.0376 17.1 0 0.643 0.708
3 openn_idea… =~ lamb… -0.594 0.0497 -11.9 0 -0.594 -0.511
4 openn_idea… =~ lamb… -0.492 0.0626 -7.86 4.00e-15 -0.492 -0.349
5 openn_idea… =~ lamb… -0.470 0.0500 -9.40 0 -0.470 -0.417
6 openn_idea… =~ lamb… -0.417 0.0537 -7.76 8.22e-15 -0.417 -0.345
7 openn_idea… =~ lamb… 0.356 0.0310 11.5 0 0.356 0.503
8 openn_idea… =~ lamb… 0.355 0.0327 10.8 0 0.355 0.475
9 openn_idea… =~ lamb… -0.327 0.0472 -6.93 4.27e-12 -0.327 -0.314
10 openn_idea… =~ lamb… 0.319 0.0467 6.83 8.55e-12 0.319 0.305
11 openn_idea… =~ lamb… 0.308 0.0281 10.9 0 0.308 0.489
12 openn_idea… =~ lamb… 0.0889 0.0670 1.33 1.84e- 1 0.0889 0.0606
# ℹ 2 more variables: std.nox <dbl>, abs_loading <dbl>Reverse-code and form parcels (T1 and T2):
df_sbsa_wide_pers <- df_sbsa_wide_pers %>%
mutate(bf06_50_t1_r = bf06_50_t1,
bf06_55_t1_r = bf06_55_t1,
bf06_30_t1_r = bf06_30_t1,
bf06_25_t1_r = bf06_25_t1,
bf06_45_t1_r = bf06_45_t1,
bf06_05_t1_r = bf06_05_t1,
bf06_50_t2_r = bf06_50_t2,
bf06_55_t2_r = bf06_55_t2,
bf06_30_t2_r = bf06_30_t2,
bf06_25_t2_r = bf06_25_t2,
bf06_45_t2_r = bf06_45_t2,
bf06_05_t2_r = bf06_05_t2) %>%
mutate(across(c(bf06_50_t1_r, bf06_55_t1_r, bf06_30_t1_r, bf06_25_t1_r, bf06_45_t1_r, bf06_05_t1_r,
bf06_50_t2_r, bf06_55_t2_r, bf06_30_t2_r, bf06_25_t2_r, bf06_45_t2_r, bf06_05_t2_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(openn_ideal_par1_t1 = rowMeans(across(c(bf06_20_t1, bf06_55_t1_r, bf06_45_t1_r, bf06_05_t1)), na.rm=T),
openn_ideal_par2_t1 = rowMeans(across(c(bf06_35_t1, bf06_25_t1_r, bf06_10_t1, bf06_15_t1)), na.rm=T),
openn_ideal_par3_t1 = rowMeans(across(c(bf06_50_t1_r, bf06_30_t1_r, bf06_60_t1, bf06_40_t1)), na.rm=T),
openn_ideal_par1_t2 = rowMeans(across(c(bf06_20_t2, bf06_55_t2_r, bf06_45_t2_r, bf06_05_t2)), na.rm=T),
openn_ideal_par2_t2 = rowMeans(across(c(bf06_35_t2, bf06_25_t2_r, bf06_10_t2, bf06_15_t2)), na.rm=T),
openn_ideal_par3_t2 = rowMeans(across(c(bf06_50_t2_r, bf06_30_t2_r, bf06_60_t2, bf06_40_t2)), na.rm=T))Testing for measurement invariance of the latent traits across time.
Satisfaction with life scale (only the first four items - last one taps into a somewhat different construct)
Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_swls <- '
# Define the latent factors
swls1 =~ NA*sw06_01_t1 + lambda1*sw06_01_t1 + sw06_02_t1 + sw06_03_t1 + sw06_04_t1
swls2 =~ NA*sw06_01_t2 + lambda1*sw06_01_t2 + sw06_02_t2 + sw06_03_t2 + sw06_04_t2
# Intercepts
sw06_01_t1 ~ i1*1
sw06_02_t1 ~ 1
sw06_03_t1 ~ 1
sw06_04_t1 ~ 1
sw06_01_t2 ~ i1*1
sw06_02_t2 ~ 1
sw06_03_t2 ~ 1
sw06_04_t2 ~ 1
# Unique Variances
sw06_01_t1 ~~ sw06_01_t1
sw06_02_t1 ~~ sw06_02_t1
sw06_03_t1 ~~ sw06_03_t1
sw06_04_t1 ~~ sw06_04_t1
sw06_01_t2 ~~ sw06_01_t2
sw06_02_t2 ~~ sw06_02_t2
sw06_03_t2 ~~ sw06_03_t2
sw06_04_t2 ~~ sw06_04_t2
# Latent Variable Means
swls1 ~ 0*1
swls2 ~ 1
# Latent Variable Variances and Covariance
swls1 ~~ 1*swls1
swls2 ~~ swls2
swls1 ~~ swls2
'
fit_configural_swls <- cfa(configural_swls, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_configural_swls, fit.measures = TRUE)
# Weak invariance model
weak_swls <- '
# Define the latent factors
swls1 =~ NA*sw06_01_t1 + lambda1*sw06_01_t1 + lambda2*sw06_02_t1 + lambda3*sw06_03_t1 + lambda4*sw06_04_t1
swls2 =~ NA*sw06_01_t2 + lambda1*sw06_01_t2 + lambda2*sw06_02_t2 + lambda3*sw06_03_t2 + lambda4*sw06_04_t2
# Intercepts
sw06_01_t1 ~ i1*1
sw06_02_t1 ~ 1
sw06_03_t1 ~ 1
sw06_04_t1 ~ 1
sw06_01_t2 ~ i1*1
sw06_02_t2 ~ 1
sw06_03_t2 ~ 1
sw06_04_t2 ~ 1
# Unique Variances
sw06_01_t1 ~~ sw06_01_t1
sw06_02_t1 ~~ sw06_02_t1
sw06_03_t1 ~~ sw06_03_t1
sw06_04_t1 ~~ sw06_04_t1
sw06_01_t2 ~~ sw06_01_t2
sw06_02_t2 ~~ sw06_02_t2
sw06_03_t2 ~~ sw06_03_t2
sw06_04_t2 ~~ sw06_04_t2
# Latent Variable Means
swls1 ~ 0*1
swls2 ~ 1
# Latent Variable Variances and Covariance
swls1 ~~ 1*swls1
swls2 ~~ swls2
swls1 ~~ swls2
'
fit_weak_swls <- cfa(weak_swls, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_weak_swls, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_swls <- '
# Define the latent factors
swls1 =~ NA*sw06_01_t1 + lambda1*sw06_01_t1 + lambda2*sw06_02_t1 + lambda3*sw06_03_t1 + lambda4*sw06_04_t1
swls2 =~ NA*sw06_01_t2 + lambda1*sw06_01_t2 + lambda2*sw06_02_t2 + lambda3*sw06_03_t2 + lambda4*sw06_04_t2
# Intercepts
sw06_01_t1 ~ i1*1
sw06_02_t1 ~ i2*1
sw06_03_t1 ~ i3*1
sw06_04_t1 ~ i4*1
sw06_01_t2 ~ i1*1
sw06_02_t2 ~ i2*1
sw06_03_t2 ~ i3*1
sw06_04_t2 ~ i4*1
# Unique Variances
sw06_01_t1 ~~ sw06_01_t1
sw06_02_t1 ~~ sw06_02_t1
sw06_03_t1 ~~ sw06_03_t1
sw06_04_t1 ~~ sw06_04_t1
sw06_01_t2 ~~ sw06_01_t2
sw06_02_t2 ~~ sw06_02_t2
sw06_03_t2 ~~ sw06_03_t2
sw06_04_t2 ~~ sw06_04_t2
# Latent Variable Means
swls1 ~ 0*1
swls2 ~ 1
# Latent Variable Variances and Covariance
swls1 ~~ 1*swls1
swls2 ~~ swls2
swls1 ~~ swls2
'
fit_strong_swls <- cfa(strong_swls, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_strong_swls, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_swls <- '
# Define the latent factors
swls1 =~ NA*sw06_01_t1 + lambda1*sw06_01_t1 + lambda2*sw06_02_t1 + lambda3*sw06_03_t1 + lambda4*sw06_04_t1
swls2 =~ NA*sw06_01_t2 + lambda1*sw06_01_t2 + lambda2*sw06_02_t2 + lambda3*sw06_03_t2 + lambda4*sw06_04_t2
# Intercepts
sw06_01_t1 ~ i1*1
sw06_02_t1 ~ i2*1
sw06_03_t1 ~ i3*1
sw06_04_t1 ~ i4*1
sw06_01_t2 ~ i1*1
sw06_02_t2 ~ i2*1
sw06_03_t2 ~ i3*1
sw06_04_t2 ~ i4*1
# Unique Variances
sw06_01_t1 ~~ u1*sw06_01_t1
sw06_02_t1 ~~ u2*sw06_02_t1
sw06_03_t1 ~~ u3*sw06_03_t1
sw06_04_t1 ~~ u4*sw06_04_t1
sw06_01_t2 ~~ u1*sw06_01_t2
sw06_02_t2 ~~ u2*sw06_02_t2
sw06_03_t2 ~~ u3*sw06_03_t2
sw06_04_t2 ~~ u4*sw06_04_t2
# Latent Variable Means
swls1 ~ 0*1
swls2 ~ 1
# Latent Variable Variances and Covariance
swls1 ~~ 1*swls1
swls2 ~~ swls2
swls1 ~~ swls2
'
fit_strict_swls <- cfa(strict_swls, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_strict_swls, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_swls) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_swls) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_swls) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_swls) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 618 25 313. 12839. 12949. 0.902 0.855 0.158 0.0486
2 weak 618 22 313. 12833. 12930. 0.903 0.876 0.146 0.0487
3 strong 618 19 314. 12828. 12912. 0.903 0.892 0.137 0.0487
4 strict 618 15 327. 12833. 12899. 0.900 0.904 0.129 0.0516# chi-square difference test for nested models
anova(fit_configural_swls, fit_weak_swls)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_configural_swls 19 12839 12949 312.52
fit_weak_swls 22 12833 12930 312.92 0.39766 0 3 0.9407anova(fit_weak_swls, fit_strong_swls)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_weak_swls 22 12833 12930 312.92
fit_strong_swls 25 12828 12912 314.12 1.2015 0 3 0.7526anova(fit_strong_swls, fit_strict_swls)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_strong_swls 25 12828 12912 314.12
fit_strict_swls 29 12833 12899 326.51 12.389 0.058253 4 0.01468 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_meaning <- '
# Define the latent factors
meaning1 =~ NA*meaning_par1_t1 + lambda1*meaning_par1_t1 + meaning_par2_t1 + meaning_par3_t1
meaning2 =~ NA*meaning_par1_t2 + lambda1*meaning_par1_t2 + meaning_par2_t2 + meaning_par3_t2
# Intercepts
meaning_par1_t1 ~ i1*1
meaning_par2_t1 ~ 1
meaning_par3_t1 ~ 1
meaning_par1_t2 ~ i1*1
meaning_par2_t2 ~ 1
meaning_par3_t2 ~ 1
# Unique Variances
meaning_par1_t1 ~~ meaning_par1_t1
meaning_par2_t1 ~~ meaning_par2_t1
meaning_par3_t1 ~~ meaning_par3_t1
meaning_par1_t2 ~~ meaning_par1_t2
meaning_par2_t2 ~~ meaning_par2_t2
meaning_par3_t2 ~~ meaning_par3_t2
# Latent Variable Means
meaning1 ~ 0*1
meaning2 ~ 1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
meaning2 ~~ meaning2
meaning1 ~~ meaning2
'
fit_configural_meaning <- cfa(configural_meaning, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_configural_meaning, fit.measures = TRUE)
# Weak invariance model
weak_meaning <- '
# Define the latent factors
meaning1 =~ NA*meaning_par1_t1 + lambda1*meaning_par1_t1 + lambda2*meaning_par2_t1 + lambda3*meaning_par3_t1
meaning2 =~ NA*meaning_par1_t2 + lambda1*meaning_par1_t2 + lambda2*meaning_par2_t2 + lambda3*meaning_par3_t2
# Intercepts
meaning_par1_t1 ~ i1*1
meaning_par2_t1 ~ 1
meaning_par3_t1 ~ 1
meaning_par1_t2 ~ i1*1
meaning_par2_t2 ~ 1
meaning_par3_t2 ~ 1
# Unique Variances
meaning_par1_t1 ~~ meaning_par1_t1
meaning_par2_t1 ~~ meaning_par2_t1
meaning_par3_t1 ~~ meaning_par3_t1
meaning_par1_t2 ~~ meaning_par1_t2
meaning_par2_t2 ~~ meaning_par2_t2
meaning_par3_t2 ~~ meaning_par3_t2
# Latent Variable Means
meaning1 ~ 0*1
meaning2 ~ 1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
meaning2 ~~ meaning2
meaning1 ~~ meaning2
'
fit_weak_meaning <- cfa(weak_meaning, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_weak_meaning, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_meaning <- '
# Define the latent factors
meaning1 =~ NA*meaning_par1_t1 + lambda1*meaning_par1_t1 + lambda2*meaning_par2_t1 + lambda3*meaning_par3_t1
meaning2 =~ NA*meaning_par1_t2 + lambda1*meaning_par1_t2 + lambda2*meaning_par2_t2 + lambda3*meaning_par3_t2
# Intercepts
meaning_par1_t1 ~ i1*1
meaning_par2_t1 ~ i2*1
meaning_par3_t1 ~ i3*1
meaning_par1_t2 ~ i1*1
meaning_par2_t2 ~ i2*1
meaning_par3_t2 ~ i3*1
# Unique Variances
meaning_par1_t1 ~~ meaning_par1_t1
meaning_par2_t1 ~~ meaning_par2_t1
meaning_par3_t1 ~~ meaning_par3_t1
meaning_par1_t2 ~~ meaning_par1_t2
meaning_par2_t2 ~~ meaning_par2_t2
meaning_par3_t2 ~~ meaning_par3_t2
# Latent Variable Means
meaning1 ~ 0*1
meaning2 ~ 1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
meaning2 ~~ meaning2
meaning1 ~~ meaning2
'
fit_strong_meaning <- cfa(strong_meaning, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_strong_meaning, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_meaning <- '
# Define the latent factors
meaning1 =~ NA*meaning_par1_t1 + lambda1*meaning_par1_t1 + lambda2*meaning_par2_t1 + lambda3*meaning_par3_t1
meaning2 =~ NA*meaning_par1_t2 + lambda1*meaning_par1_t2 + lambda2*meaning_par2_t2 + lambda3*meaning_par3_t2
# Intercepts
meaning_par1_t1 ~ i1*1
meaning_par2_t1 ~ i2*1
meaning_par3_t1 ~ i3*1
meaning_par1_t2 ~ i1*1
meaning_par2_t2 ~ i2*1
meaning_par3_t2 ~ i3*1
# Unique Variances
meaning_par1_t1 ~~ u1*meaning_par1_t1
meaning_par2_t1 ~~ u2*meaning_par2_t1
meaning_par3_t1 ~~ u3*meaning_par3_t1
meaning_par1_t2 ~~ u1*meaning_par1_t2
meaning_par2_t2 ~~ u2*meaning_par2_t2
meaning_par3_t2 ~~ u3*meaning_par3_t2
# Latent Variable Means
meaning1 ~ 0*1
meaning2 ~ 1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
meaning2 ~~ meaning2
meaning1 ~~ meaning2
'
fit_strict_meaning <- cfa(strict_meaning, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_strict_meaning, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_meaning) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_meaning) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_meaning) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_meaning) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 618 19 220. 9460. 9544. 0.923 0.855 0.207 0.0380
2 weak 618 17 223. 9459. 9535. 0.922 0.883 0.186 0.0419
3 strong 618 15 224. 9456. 9523. 0.923 0.903 0.169 0.0411
4 strict 618 12 226. 9452. 9505. 0.923 0.923 0.151 0.0408# chi-square difference test for nested models
anova(fit_configural_meaning, fit_weak_meaning)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_meaning 8 9460.3 9544.4 219.72
fit_weak_meaning 10 9459.4 9534.6 222.76 3.0424 0.02904 2
Pr(>Chisq)
fit_configural_meaning
fit_weak_meaning 0.2185anova(fit_weak_meaning, fit_strong_meaning)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_weak_meaning 10 9459.4 9534.6 222.76
fit_strong_meaning 12 9456.2 9522.6 223.54 0.78133 0 2 0.6766anova(fit_strong_meaning, fit_strict_meaning)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_strong_meaning 12 9456.2 9522.6 223.54
fit_strict_meaning 15 9452.3 9505.4 225.70 2.1552 0 3 0.5408Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_selfes <- '
# Define the latent factors
selfes1 =~ NA*selfes_par1_t1 + lambda1*selfes_par1_t1 + selfes_par2_t1 + selfes_par3_t1
selfes2 =~ NA*selfes_par1_t2 + lambda1*selfes_par1_t2 + selfes_par2_t2 + selfes_par3_t2
# Intercepts
selfes_par1_t1 ~ i1*1
selfes_par2_t1 ~ 1
selfes_par3_t1 ~ 1
selfes_par1_t2 ~ i1*1
selfes_par2_t2 ~ 1
selfes_par3_t2 ~ 1
# Unique Variances
selfes_par1_t1 ~~ selfes_par1_t1
selfes_par2_t1 ~~ selfes_par2_t1
selfes_par3_t1 ~~ selfes_par3_t1
selfes_par1_t2 ~~ selfes_par1_t2
selfes_par2_t2 ~~ selfes_par2_t2
selfes_par3_t2 ~~ selfes_par3_t2
# Latent Variable Means
selfes1 ~ 0*1
selfes2 ~ 1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
selfes2 ~~ selfes2
selfes1 ~~ selfes2
'
fit_configural_selfes <- cfa(configural_selfes, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_configural_selfes, fit.measures = TRUE)
# Weak invariance model
weak_selfes <- '
# Define the latent factors
selfes1 =~ NA*selfes_par1_t1 + lambda1*selfes_par1_t1 + lambda2*selfes_par2_t1 + lambda3*selfes_par3_t1
selfes2 =~ NA*selfes_par1_t2 + lambda1*selfes_par1_t2 + lambda2*selfes_par2_t2 + lambda3*selfes_par3_t2
# Intercepts
selfes_par1_t1 ~ i1*1
selfes_par2_t1 ~ 1
selfes_par3_t1 ~ 1
selfes_par1_t2 ~ i1*1
selfes_par2_t2 ~ 1
selfes_par3_t2 ~ 1
# Unique Variances
selfes_par1_t1 ~~ selfes_par1_t1
selfes_par2_t1 ~~ selfes_par2_t1
selfes_par3_t1 ~~ selfes_par3_t1
selfes_par1_t2 ~~ selfes_par1_t2
selfes_par2_t2 ~~ selfes_par2_t2
selfes_par3_t2 ~~ selfes_par3_t2
# Latent Variable Means
selfes1 ~ 0*1
selfes2 ~ 1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
selfes2 ~~ selfes2
selfes1 ~~ selfes2
'
fit_weak_selfes <- cfa(weak_selfes, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_weak_selfes, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_selfes <- '
# Define the latent factors
selfes1 =~ NA*selfes_par1_t1 + lambda1*selfes_par1_t1 + lambda2*selfes_par2_t1 + lambda3*selfes_par3_t1
selfes2 =~ NA*selfes_par1_t2 + lambda1*selfes_par1_t2 + lambda2*selfes_par2_t2 + lambda3*selfes_par3_t2
# Intercepts
selfes_par1_t1 ~ i1*1
selfes_par2_t1 ~ i2*1
selfes_par3_t1 ~ i3*1
selfes_par1_t2 ~ i1*1
selfes_par2_t2 ~ i2*1
selfes_par3_t2 ~ i3*1
# Unique Variances
selfes_par1_t1 ~~ selfes_par1_t1
selfes_par2_t1 ~~ selfes_par2_t1
selfes_par3_t1 ~~ selfes_par3_t1
selfes_par1_t2 ~~ selfes_par1_t2
selfes_par2_t2 ~~ selfes_par2_t2
selfes_par3_t2 ~~ selfes_par3_t2
# Latent Variable Means
selfes1 ~ 0*1
selfes2 ~ 1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
selfes2 ~~ selfes2
selfes1 ~~ selfes2
'
fit_strong_selfes <- cfa(strong_selfes, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_strong_selfes, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_selfes <- '
# Define the latent factors
selfes1 =~ NA*selfes_par1_t1 + lambda1*selfes_par1_t1 + lambda2*selfes_par2_t1 + lambda3*selfes_par3_t1
selfes2 =~ NA*selfes_par1_t2 + lambda1*selfes_par1_t2 + lambda2*selfes_par2_t2 + lambda3*selfes_par3_t2
# Intercepts
selfes_par1_t1 ~ i1*1
selfes_par2_t1 ~ i2*1
selfes_par3_t1 ~ i3*1
selfes_par1_t2 ~ i1*1
selfes_par2_t2 ~ i2*1
selfes_par3_t2 ~ i3*1
# Unique Variances
selfes_par1_t1 ~~ u1*selfes_par1_t1
selfes_par2_t1 ~~ u2*selfes_par2_t1
selfes_par3_t1 ~~ u3*selfes_par3_t1
selfes_par1_t2 ~~ u1*selfes_par1_t2
selfes_par2_t2 ~~ u2*selfes_par2_t2
selfes_par3_t2 ~~ u3*selfes_par3_t2
# Latent Variable Means
selfes1 ~ 0*1
selfes2 ~ 1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
selfes2 ~~ selfes2
selfes1 ~~ selfes2
'
fit_strict_selfes <- cfa(strict_selfes, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_strict_selfes, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_selfes) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_selfes) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_selfes) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_selfes) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 618 19 127. 6608. 6692. 0.966 0.936 0.155 0.0181
2 weak 618 17 128. 6605. 6680. 0.966 0.950 0.138 0.0209
3 strong 618 15 128. 6601. 6668. 0.967 0.959 0.125 0.0220
4 strict 618 12 131. 6598. 6651. 0.967 0.967 0.112 0.0203# chi-square difference test for nested models
anova(fit_configural_selfes, fit_weak_selfes)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_selfes 8 6608.2 6692.3 126.91
fit_weak_selfes 10 6604.9 6680.2 127.65 0.7353 0 2
Pr(>Chisq)
fit_configural_selfes
fit_weak_selfes 0.6924anova(fit_weak_selfes, fit_strong_selfes)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_weak_selfes 10 6604.9 6680.2 127.65
fit_strong_selfes 12 6601.3 6667.7 128.05 0.40196 0 2 0.8179anova(fit_strong_selfes, fit_strict_selfes)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_strong_selfes 12 6601.3 6667.7 128.05
fit_strict_selfes 15 6598.3 6651.4 131.03 2.9746 0 3 0.3956Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_concept <- '
# Define the latent factors
concept1 =~ NA*concept_par1_t1 + lambda1*concept_par1_t1 + concept_par2_t1 + concept_par3_t1
concept2 =~ NA*concept_par1_t2 + lambda1*concept_par1_t2 + concept_par2_t2 + concept_par3_t2
# Intercepts
concept_par1_t1 ~ i1*1
concept_par2_t1 ~ 1
concept_par3_t1 ~ 1
concept_par1_t2 ~ i1*1
concept_par2_t2 ~ 1
concept_par3_t2 ~ 1
# Unique Variances
concept_par1_t1 ~~ concept_par1_t1
concept_par2_t1 ~~ concept_par2_t1
concept_par3_t1 ~~ concept_par3_t1
concept_par1_t2 ~~ concept_par1_t2
concept_par2_t2 ~~ concept_par2_t2
concept_par3_t2 ~~ concept_par3_t2
# Latent Variable Means
concept1 ~ 0*1
concept2 ~ 1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
concept2 ~~ concept2
concept1 ~~ concept2
'
fit_configural_concept <- cfa(configural_concept, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_configural_concept, fit.measures = TRUE)
# Weak invariance model
weak_concept <- '
# Define the latent factors
concept1 =~ NA*concept_par1_t1 + lambda1*concept_par1_t1 + lambda2*concept_par2_t1 + lambda3*concept_par3_t1
concept2 =~ NA*concept_par1_t2 + lambda1*concept_par1_t2 + lambda2*concept_par2_t2 + lambda3*concept_par3_t2
# Intercepts
concept_par1_t1 ~ i1*1
concept_par2_t1 ~ 1
concept_par3_t1 ~ 1
concept_par1_t2 ~ i1*1
concept_par2_t2 ~ 1
concept_par3_t2 ~ 1
# Unique Variances
concept_par1_t1 ~~ concept_par1_t1
concept_par2_t1 ~~ concept_par2_t1
concept_par3_t1 ~~ concept_par3_t1
concept_par1_t2 ~~ concept_par1_t2
concept_par2_t2 ~~ concept_par2_t2
concept_par3_t2 ~~ concept_par3_t2
# Latent Variable Means
concept1 ~ 0*1
concept2 ~ 1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
concept2 ~~ concept2
concept1 ~~ concept2
'
fit_weak_concept <- cfa(weak_concept, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_weak_concept, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_concept <- '
# Define the latent factors
concept1 =~ NA*concept_par1_t1 + lambda1*concept_par1_t1 + lambda2*concept_par2_t1 + lambda3*concept_par3_t1
concept2 =~ NA*concept_par1_t2 + lambda1*concept_par1_t2 + lambda2*concept_par2_t2 + lambda3*concept_par3_t2
# Intercepts
concept_par1_t1 ~ i1*1
concept_par2_t1 ~ i2*1
concept_par3_t1 ~ i3*1
concept_par1_t2 ~ i1*1
concept_par2_t2 ~ i2*1
concept_par3_t2 ~ i3*1
# Unique Variances
concept_par1_t1 ~~ concept_par1_t1
concept_par2_t1 ~~ concept_par2_t1
concept_par3_t1 ~~ concept_par3_t1
concept_par1_t2 ~~ concept_par1_t2
concept_par2_t2 ~~ concept_par2_t2
concept_par3_t2 ~~ concept_par3_t2
# Latent Variable Means
concept1 ~ 0*1
concept2 ~ 1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
concept2 ~~ concept2
concept1 ~~ concept2
'
fit_strong_concept <- cfa(strong_concept, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_strong_concept, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_concept <- '
# Define the latent factors
concept1 =~ NA*concept_par1_t1 + lambda1*concept_par1_t1 + lambda2*concept_par2_t1 + lambda3*concept_par3_t1
concept2 =~ NA*concept_par1_t2 + lambda1*concept_par1_t2 + lambda2*concept_par2_t2 + lambda3*concept_par3_t2
# Intercepts
concept_par1_t1 ~ i1*1
concept_par2_t1 ~ i2*1
concept_par3_t1 ~ i3*1
concept_par1_t2 ~ i1*1
concept_par2_t2 ~ i2*1
concept_par3_t2 ~ i3*1
# Unique Variances
concept_par1_t1 ~~ u1*concept_par1_t1
concept_par2_t1 ~~ u2*concept_par2_t1
concept_par3_t1 ~~ u3*concept_par3_t1
concept_par1_t2 ~~ u1*concept_par1_t2
concept_par2_t2 ~~ u2*concept_par2_t2
concept_par3_t2 ~~ u3*concept_par3_t2
# Latent Variable Means
concept1 ~ 0*1
concept2 ~ 1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
concept2 ~~ concept2
concept1 ~~ concept2
'
fit_strict_concept <- cfa(strict_concept, data = df_sbsa_wide_wb, mimic = "mplus")
summary(fit_strict_concept, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_concept) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_concept) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_concept) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_concept) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 618 19 132. 7652. 7737. 0.949 0.904 0.159 0.0267
2 weak 618 17 133. 7649. 7724. 0.950 0.925 0.141 0.0283
3 strong 618 15 133. 7645. 7711. 0.950 0.938 0.128 0.0280
4 strict 618 12 136. 7642. 7695. 0.950 0.950 0.114 0.0296# chi-square difference test for nested models
anova(fit_configural_concept, fit_weak_concept)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_concept 8 7652.4 7736.5 132.39
fit_weak_concept 10 7648.8 7724.0 132.78 0.38786 0 2
Pr(>Chisq)
fit_configural_concept
fit_weak_concept 0.8237anova(fit_weak_concept, fit_strong_concept)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_weak_concept 10 7648.8 7724.0 132.78
fit_strong_concept 12 7644.9 7711.3 132.88 0.10064 0 2 0.9509anova(fit_strong_concept, fit_strict_concept)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_concept 12 7644.9 7711.3 132.88
fit_strict_concept 15 7642.2 7695.4 136.22 3.3458 0.013657 3
Pr(>Chisq)
fit_strong_concept
fit_strict_concept 0.3413Testing for measurement invariance of the latent BFI-2 traits across time.
Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t1 + lambda1*extra_curr_par1_t1 + extra_curr_par2_t1 + extra_curr_par3_t1
extra_curr2 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + extra_curr_par2_t2 + extra_curr_par3_t2
# Intercepts
extra_curr_par1_t1 ~ i1*1
extra_curr_par2_t1 ~ 1
extra_curr_par3_t1 ~ 1
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ 1
extra_curr_par3_t2 ~ 1
# Unique Variances
extra_curr_par1_t1 ~~ extra_curr_par1_t1
extra_curr_par2_t1 ~~ extra_curr_par2_t1
extra_curr_par3_t1 ~~ extra_curr_par3_t1
extra_curr_par1_t2 ~~ extra_curr_par1_t2
extra_curr_par2_t2 ~~ extra_curr_par2_t2
extra_curr_par3_t2 ~~ extra_curr_par3_t2
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_configural_extra_curr <- cfa(configural_extra_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_configural_extra_curr, fit.measures = TRUE)
# Weak invariance model
weak_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t1 + lambda1*extra_curr_par1_t1 + lambda2*extra_curr_par2_t1 + lambda3*extra_curr_par3_t1
extra_curr2 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + lambda2*extra_curr_par2_t2 + lambda3*extra_curr_par3_t2
# Intercepts
extra_curr_par1_t1 ~ i1*1
extra_curr_par2_t1 ~ 1
extra_curr_par3_t1 ~ 1
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ 1
extra_curr_par3_t2 ~ 1
# Unique Variances
extra_curr_par1_t1 ~~ extra_curr_par1_t1
extra_curr_par2_t1 ~~ extra_curr_par2_t1
extra_curr_par3_t1 ~~ extra_curr_par3_t1
extra_curr_par1_t2 ~~ extra_curr_par1_t2
extra_curr_par2_t2 ~~ extra_curr_par2_t2
extra_curr_par3_t2 ~~ extra_curr_par3_t2
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_weak_extra_curr <- cfa(weak_extra_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_weak_extra_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t1 + lambda1*extra_curr_par1_t1 + lambda2*extra_curr_par2_t1 + lambda3*extra_curr_par3_t1
extra_curr2 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + lambda2*extra_curr_par2_t2 + lambda3*extra_curr_par3_t2
# Intercepts
extra_curr_par1_t1 ~ i1*1
extra_curr_par2_t1 ~ i2*1
extra_curr_par3_t1 ~ i3*1
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ i2*1
extra_curr_par3_t2 ~ i3*1
# Unique Variances
extra_curr_par1_t1 ~~ extra_curr_par1_t1
extra_curr_par2_t1 ~~ extra_curr_par2_t1
extra_curr_par3_t1 ~~ extra_curr_par3_t1
extra_curr_par1_t2 ~~ extra_curr_par1_t2
extra_curr_par2_t2 ~~ extra_curr_par2_t2
extra_curr_par3_t2 ~~ extra_curr_par3_t2
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_strong_extra_curr <- cfa(strong_extra_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strong_extra_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t1 + lambda1*extra_curr_par1_t1 + lambda2*extra_curr_par2_t1 + lambda3*extra_curr_par3_t1
extra_curr2 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + lambda2*extra_curr_par2_t2 + lambda3*extra_curr_par3_t2
# Intercepts
extra_curr_par1_t1 ~ i1*1
extra_curr_par2_t1 ~ i2*1
extra_curr_par3_t1 ~ i3*1
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ i2*1
extra_curr_par3_t2 ~ i3*1
# Unique Variances
extra_curr_par1_t1 ~~ u1*extra_curr_par1_t1
extra_curr_par2_t1 ~~ u2*extra_curr_par2_t1
extra_curr_par3_t1 ~~ u3*extra_curr_par3_t1
extra_curr_par1_t2 ~~ u1*extra_curr_par1_t2
extra_curr_par2_t2 ~~ u2*extra_curr_par2_t2
extra_curr_par3_t2 ~~ u3*extra_curr_par3_t2
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_strict_extra_curr <- cfa(strict_extra_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strict_extra_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 619 19 316. 6840. 6924. 0.870 0.757 0.249 0.0490
2 weak 619 17 318. 6838. 6913. 0.870 0.806 0.223 0.0534
3 strong 619 15 318. 6835. 6901. 0.871 0.839 0.203 0.0548
4 strict 619 12 320. 6830. 6884. 0.871 0.871 0.181 0.0531# chi-square difference test for nested models
anova(fit_configural_extra_curr, fit_weak_extra_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_extra_curr 8 6840.2 6924.3 315.84
fit_weak_extra_curr 10 6838.0 6913.3 317.61 1.7675 0 2
Pr(>Chisq)
fit_configural_extra_curr
fit_weak_extra_curr 0.4132anova(fit_weak_extra_curr, fit_strong_extra_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_extra_curr 10 6838.0 6913.3 317.61
fit_strong_extra_curr 12 6834.7 6901.1 318.31 0.69545 0 2
Pr(>Chisq)
fit_weak_extra_curr
fit_strong_extra_curr 0.7063anova(fit_strong_extra_curr, fit_strict_extra_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_extra_curr 12 6834.7 6901.1 318.31
fit_strict_extra_curr 15 6830.4 6883.6 320.06 1.7554 0 3
Pr(>Chisq)
fit_strong_extra_curr
fit_strict_extra_curr 0.6247Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t1 + lambda1*extra_ideal_par1_t1 + extra_ideal_par2_t1 + extra_ideal_par3_t1
extra_ideal2 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + extra_ideal_par2_t2 + extra_ideal_par3_t2
# Intercepts
extra_ideal_par1_t1 ~ i1*1
extra_ideal_par2_t1 ~ 1
extra_ideal_par3_t1 ~ 1
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ 1
extra_ideal_par3_t2 ~ 1
# Unique Variances
extra_ideal_par1_t1 ~~ extra_ideal_par1_t1
extra_ideal_par2_t1 ~~ extra_ideal_par2_t1
extra_ideal_par3_t1 ~~ extra_ideal_par3_t1
extra_ideal_par1_t2 ~~ extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ extra_ideal_par3_t2
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_configural_extra_ideal <- cfa(configural_extra_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_configural_extra_ideal, fit.measures = TRUE)
# Weak invariance model
weak_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t1 + lambda1*extra_ideal_par1_t1 + lambda2*extra_ideal_par2_t1 + lambda3*extra_ideal_par3_t1
extra_ideal2 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + lambda2*extra_ideal_par2_t2 + lambda3*extra_ideal_par3_t2
# Intercepts
extra_ideal_par1_t1 ~ i1*1
extra_ideal_par2_t1 ~ 1
extra_ideal_par3_t1 ~ 1
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ 1
extra_ideal_par3_t2 ~ 1
# Unique Variances
extra_ideal_par1_t1 ~~ extra_ideal_par1_t1
extra_ideal_par2_t1 ~~ extra_ideal_par2_t1
extra_ideal_par3_t1 ~~ extra_ideal_par3_t1
extra_ideal_par1_t2 ~~ extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ extra_ideal_par3_t2
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_weak_extra_ideal <- cfa(weak_extra_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_weak_extra_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t1 + lambda1*extra_ideal_par1_t1 + lambda2*extra_ideal_par2_t1 + lambda3*extra_ideal_par3_t1
extra_ideal2 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + lambda2*extra_ideal_par2_t2 + lambda3*extra_ideal_par3_t2
# Intercepts
extra_ideal_par1_t1 ~ i1*1
extra_ideal_par2_t1 ~ i2*1
extra_ideal_par3_t1 ~ i3*1
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ i2*1
extra_ideal_par3_t2 ~ i3*1
# Unique Variances
extra_ideal_par1_t1 ~~ extra_ideal_par1_t1
extra_ideal_par2_t1 ~~ extra_ideal_par2_t1
extra_ideal_par3_t1 ~~ extra_ideal_par3_t1
extra_ideal_par1_t2 ~~ extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ extra_ideal_par3_t2
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_strong_extra_ideal <- cfa(strong_extra_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strong_extra_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t1 + lambda1*extra_ideal_par1_t1 + lambda2*extra_ideal_par2_t1 + lambda3*extra_ideal_par3_t1
extra_ideal2 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + lambda2*extra_ideal_par2_t2 + lambda3*extra_ideal_par3_t2
# Intercepts
extra_ideal_par1_t1 ~ i1*1
extra_ideal_par2_t1 ~ i2*1
extra_ideal_par3_t1 ~ i3*1
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ i2*1
extra_ideal_par3_t2 ~ i3*1
# Unique Variances
extra_ideal_par1_t1 ~~ u1*extra_ideal_par1_t1
extra_ideal_par2_t1 ~~ u2*extra_ideal_par2_t1
extra_ideal_par3_t1 ~~ u3*extra_ideal_par3_t1
extra_ideal_par1_t2 ~~ u1*extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ u2*extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ u3*extra_ideal_par3_t2
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_strict_extra_ideal <- cfa(strict_extra_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strict_extra_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 619 19 118. 5672. 5757. 0.914 0.838 0.149 0.0451
2 weak 619 17 118. 5669. 5744. 0.915 0.872 0.132 0.0488
3 strong 619 15 121. 5667. 5734. 0.915 0.893 0.121 0.0470
4 strict 619 12 130. 5670. 5723. 0.910 0.910 0.111 0.0818# chi-square difference test for nested models
anova(fit_configural_extra_ideal, fit_weak_extra_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_extra_ideal 8 5672.4 5756.6 117.64
fit_weak_extra_ideal 10 5669.1 5744.4 118.34 0.70155 0 2
Pr(>Chisq)
fit_configural_extra_ideal
fit_weak_extra_ideal 0.7041anova(fit_weak_extra_ideal, fit_strong_extra_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_extra_ideal 10 5669.1 5744.4 118.34
fit_strong_extra_ideal 12 5667.5 5733.9 120.70 2.3603 0.017059 2
Pr(>Chisq)
fit_weak_extra_ideal
fit_strong_extra_ideal 0.3072anova(fit_strong_extra_ideal, fit_strict_extra_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_extra_ideal 12 5667.5 5733.9 120.70
fit_strict_extra_ideal 15 5670.3 5723.5 129.56 8.8594 0.056172 3
Pr(>Chisq)
fit_strong_extra_ideal
fit_strict_extra_ideal 0.03122 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t1 + lambda1*agree_curr_par1_t1 + agree_curr_par2_t1 + agree_curr_par3_t1
agree_curr2 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + agree_curr_par2_t2 + agree_curr_par3_t2
# Intercepts
agree_curr_par1_t1 ~ i1*1
agree_curr_par2_t1 ~ 1
agree_curr_par3_t1 ~ 1
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ 1
agree_curr_par3_t2 ~ 1
# Unique Variances
agree_curr_par1_t1 ~~ agree_curr_par1_t1
agree_curr_par2_t1 ~~ agree_curr_par2_t1
agree_curr_par3_t1 ~~ agree_curr_par3_t1
agree_curr_par1_t2 ~~ agree_curr_par1_t2
agree_curr_par2_t2 ~~ agree_curr_par2_t2
agree_curr_par3_t2 ~~ agree_curr_par3_t2
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_configural_agree_curr <- cfa(configural_agree_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_configural_agree_curr, fit.measures = TRUE)
# Weak invariance model
weak_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t1 + lambda1*agree_curr_par1_t1 + lambda2*agree_curr_par2_t1 + lambda3*agree_curr_par3_t1
agree_curr2 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + lambda2*agree_curr_par2_t2 + lambda3*agree_curr_par3_t2
# Intercepts
agree_curr_par1_t1 ~ i1*1
agree_curr_par2_t1 ~ 1
agree_curr_par3_t1 ~ 1
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ 1
agree_curr_par3_t2 ~ 1
# Unique Variances
agree_curr_par1_t1 ~~ agree_curr_par1_t1
agree_curr_par2_t1 ~~ agree_curr_par2_t1
agree_curr_par3_t1 ~~ agree_curr_par3_t1
agree_curr_par1_t2 ~~ agree_curr_par1_t2
agree_curr_par2_t2 ~~ agree_curr_par2_t2
agree_curr_par3_t2 ~~ agree_curr_par3_t2
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_weak_agree_curr <- cfa(weak_agree_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_weak_agree_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t1 + lambda1*agree_curr_par1_t1 + lambda2*agree_curr_par2_t1 + lambda3*agree_curr_par3_t1
agree_curr2 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + lambda2*agree_curr_par2_t2 + lambda3*agree_curr_par3_t2
# Intercepts
agree_curr_par1_t1 ~ i1*1
agree_curr_par2_t1 ~ i2*1
agree_curr_par3_t1 ~ i3*1
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ i2*1
agree_curr_par3_t2 ~ i3*1
# Unique Variances
agree_curr_par1_t1 ~~ agree_curr_par1_t1
agree_curr_par2_t1 ~~ agree_curr_par2_t1
agree_curr_par3_t1 ~~ agree_curr_par3_t1
agree_curr_par1_t2 ~~ agree_curr_par1_t2
agree_curr_par2_t2 ~~ agree_curr_par2_t2
agree_curr_par3_t2 ~~ agree_curr_par3_t2
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_strong_agree_curr <- cfa(strong_agree_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strong_agree_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t1 + lambda1*agree_curr_par1_t1 + lambda2*agree_curr_par2_t1 + lambda3*agree_curr_par3_t1
agree_curr2 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + lambda2*agree_curr_par2_t2 + lambda3*agree_curr_par3_t2
# Intercepts
agree_curr_par1_t1 ~ i1*1
agree_curr_par2_t1 ~ i2*1
agree_curr_par3_t1 ~ i3*1
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ i2*1
agree_curr_par3_t2 ~ i3*1
# Unique Variances
agree_curr_par1_t1 ~~ u1*agree_curr_par1_t1
agree_curr_par2_t1 ~~ u2*agree_curr_par2_t1
agree_curr_par3_t1 ~~ u3*agree_curr_par3_t1
agree_curr_par1_t2 ~~ u1*agree_curr_par1_t2
agree_curr_par2_t2 ~~ u2*agree_curr_par2_t2
agree_curr_par3_t2 ~~ u3*agree_curr_par3_t2
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_strict_agree_curr <- cfa(strict_agree_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strict_agree_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 619 19 183. 5735. 5819. 0.888 0.789 0.188 0.0539
2 weak 619 17 184. 5732. 5807. 0.888 0.832 0.167 0.0589
3 strong 619 15 184. 5728. 5795. 0.889 0.861 0.152 0.0573
4 strict 619 12 196. 5734. 5787. 0.883 0.883 0.140 0.0722# chi-square difference test for nested models
anova(fit_configural_agree_curr, fit_weak_agree_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_agree_curr 8 5734.6 5818.7 182.58
fit_weak_agree_curr 10 5731.7 5807.0 183.64 1.0692 0 2
Pr(>Chisq)
fit_configural_agree_curr
fit_weak_agree_curr 0.5859anova(fit_weak_agree_curr, fit_strong_agree_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_agree_curr 10 5731.7 5807.0 183.64
fit_strong_agree_curr 12 5728.4 5794.9 184.39 0.74856 0 2
Pr(>Chisq)
fit_weak_agree_curr
fit_strong_agree_curr 0.6878anova(fit_strong_agree_curr, fit_strict_agree_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_agree_curr 12 5728.4 5794.9 184.39
fit_strict_agree_curr 15 5733.9 5787.1 195.91 11.515 0.067715 3
Pr(>Chisq)
fit_strong_agree_curr
fit_strict_agree_curr 0.009243 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t1 + lambda1*agree_ideal_par1_t1 + agree_ideal_par2_t1 + agree_ideal_par3_t1
agree_ideal2 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + agree_ideal_par2_t2 + agree_ideal_par3_t2
# Intercepts
agree_ideal_par1_t1 ~ i1*1
agree_ideal_par2_t1 ~ 1
agree_ideal_par3_t1 ~ 1
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ 1
agree_ideal_par3_t2 ~ 1
# Unique Variances
agree_ideal_par1_t1 ~~ agree_ideal_par1_t1
agree_ideal_par2_t1 ~~ agree_ideal_par2_t1
agree_ideal_par3_t1 ~~ agree_ideal_par3_t1
agree_ideal_par1_t2 ~~ agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ agree_ideal_par3_t2
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_configural_agree_ideal <- cfa(configural_agree_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_configural_agree_ideal, fit.measures = TRUE)
# Weak invariance model
weak_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t1 + lambda1*agree_ideal_par1_t1 + lambda2*agree_ideal_par2_t1 + lambda3*agree_ideal_par3_t1
agree_ideal2 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + lambda2*agree_ideal_par2_t2 + lambda3*agree_ideal_par3_t2
# Intercepts
agree_ideal_par1_t1 ~ i1*1
agree_ideal_par2_t1 ~ 1
agree_ideal_par3_t1 ~ 1
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ 1
agree_ideal_par3_t2 ~ 1
# Unique Variances
agree_ideal_par1_t1 ~~ agree_ideal_par1_t1
agree_ideal_par2_t1 ~~ agree_ideal_par2_t1
agree_ideal_par3_t1 ~~ agree_ideal_par3_t1
agree_ideal_par1_t2 ~~ agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ agree_ideal_par3_t2
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_weak_agree_ideal <- cfa(weak_agree_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_weak_agree_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t1 + lambda1*agree_ideal_par1_t1 + lambda2*agree_ideal_par2_t1 + lambda3*agree_ideal_par3_t1
agree_ideal2 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + lambda2*agree_ideal_par2_t2 + lambda3*agree_ideal_par3_t2
# Intercepts
agree_ideal_par1_t1 ~ i1*1
agree_ideal_par2_t1 ~ i2*1
agree_ideal_par3_t1 ~ i3*1
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ i2*1
agree_ideal_par3_t2 ~ i3*1
# Unique Variances
agree_ideal_par1_t1 ~~ agree_ideal_par1_t1
agree_ideal_par2_t1 ~~ agree_ideal_par2_t1
agree_ideal_par3_t1 ~~ agree_ideal_par3_t1
agree_ideal_par1_t2 ~~ agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ agree_ideal_par3_t2
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_strong_agree_ideal <- cfa(strong_agree_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strong_agree_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t1 + lambda1*agree_ideal_par1_t1 + lambda2*agree_ideal_par2_t1 + lambda3*agree_ideal_par3_t1
agree_ideal2 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + lambda2*agree_ideal_par2_t2 + lambda3*agree_ideal_par3_t2
# Intercepts
agree_ideal_par1_t1 ~ i1*1
agree_ideal_par2_t1 ~ i2*1
agree_ideal_par3_t1 ~ i3*1
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ i2*1
agree_ideal_par3_t2 ~ i3*1
# Unique Variances
agree_ideal_par1_t1 ~~ u1*agree_ideal_par1_t1
agree_ideal_par2_t1 ~~ u2*agree_ideal_par2_t1
agree_ideal_par3_t1 ~~ u3*agree_ideal_par3_t1
agree_ideal_par1_t2 ~~ u1*agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ u2*agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ u3*agree_ideal_par3_t2
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_strict_agree_ideal <- cfa(strict_agree_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strict_agree_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 619 19 132. 5985. 6069. 0.918 0.847 0.158 0.0445
2 weak 619 17 132. 5981. 6057. 0.919 0.879 0.140 0.0471
3 strong 619 15 132. 5978. 6044. 0.921 0.901 0.127 0.0463
4 strict 619 12 135. 5974. 6027. 0.921 0.921 0.114 0.0587# chi-square difference test for nested models
anova(fit_configural_agree_ideal, fit_weak_agree_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_agree_ideal 8 5984.9 6069.1 131.52
fit_weak_agree_ideal 10 5981.5 6056.7 132.06 0.54162 0 2
Pr(>Chisq)
fit_configural_agree_ideal
fit_weak_agree_ideal 0.7628anova(fit_weak_agree_ideal, fit_strong_agree_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_agree_ideal 10 5981.5 6056.7 132.06
fit_strong_agree_ideal 12 5977.7 6044.1 132.32 0.2588 0 2
Pr(>Chisq)
fit_weak_agree_ideal
fit_strong_agree_ideal 0.8786anova(fit_strong_agree_ideal, fit_strict_agree_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_agree_ideal 12 5977.7 6044.1 132.32
fit_strict_agree_ideal 15 5974.3 6027.4 134.89 2.568 0 3
Pr(>Chisq)
fit_strong_agree_ideal
fit_strict_agree_ideal 0.4631Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t1 + lambda1*consc_curr_par1_t1 + consc_curr_par2_t1 + consc_curr_par3_t1
consc_curr2 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + consc_curr_par2_t2 + consc_curr_par3_t2
# Intercepts
consc_curr_par1_t1 ~ i1*1
consc_curr_par2_t1 ~ 1
consc_curr_par3_t1 ~ 1
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ 1
consc_curr_par3_t2 ~ 1
# Unique Variances
consc_curr_par1_t1 ~~ consc_curr_par1_t1
consc_curr_par2_t1 ~~ consc_curr_par2_t1
consc_curr_par3_t1 ~~ consc_curr_par3_t1
consc_curr_par1_t2 ~~ consc_curr_par1_t2
consc_curr_par2_t2 ~~ consc_curr_par2_t2
consc_curr_par3_t2 ~~ consc_curr_par3_t2
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_configural_consc_curr <- cfa(configural_consc_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_configural_consc_curr, fit.measures = TRUE)
# Weak invariance model
weak_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t1 + lambda1*consc_curr_par1_t1 + lambda2*consc_curr_par2_t1 + lambda3*consc_curr_par3_t1
consc_curr2 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + lambda2*consc_curr_par2_t2 + lambda3*consc_curr_par3_t2
# Intercepts
consc_curr_par1_t1 ~ i1*1
consc_curr_par2_t1 ~ 1
consc_curr_par3_t1 ~ 1
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ 1
consc_curr_par3_t2 ~ 1
# Unique Variances
consc_curr_par1_t1 ~~ consc_curr_par1_t1
consc_curr_par2_t1 ~~ consc_curr_par2_t1
consc_curr_par3_t1 ~~ consc_curr_par3_t1
consc_curr_par1_t2 ~~ consc_curr_par1_t2
consc_curr_par2_t2 ~~ consc_curr_par2_t2
consc_curr_par3_t2 ~~ consc_curr_par3_t2
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_weak_consc_curr <- cfa(weak_consc_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_weak_consc_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t1 + lambda1*consc_curr_par1_t1 + lambda2*consc_curr_par2_t1 + lambda3*consc_curr_par3_t1
consc_curr2 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + lambda2*consc_curr_par2_t2 + lambda3*consc_curr_par3_t2
# Intercepts
consc_curr_par1_t1 ~ i1*1
consc_curr_par2_t1 ~ i2*1
consc_curr_par3_t1 ~ i3*1
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ i2*1
consc_curr_par3_t2 ~ i3*1
# Unique Variances
consc_curr_par1_t1 ~~ consc_curr_par1_t1
consc_curr_par2_t1 ~~ consc_curr_par2_t1
consc_curr_par3_t1 ~~ consc_curr_par3_t1
consc_curr_par1_t2 ~~ consc_curr_par1_t2
consc_curr_par2_t2 ~~ consc_curr_par2_t2
consc_curr_par3_t2 ~~ consc_curr_par3_t2
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_strong_consc_curr <- cfa(strong_consc_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strong_consc_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t1 + lambda1*consc_curr_par1_t1 + lambda2*consc_curr_par2_t1 + lambda3*consc_curr_par3_t1
consc_curr2 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + lambda2*consc_curr_par2_t2 + lambda3*consc_curr_par3_t2
# Intercepts
consc_curr_par1_t1 ~ i1*1
consc_curr_par2_t1 ~ i2*1
consc_curr_par3_t1 ~ i3*1
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ i2*1
consc_curr_par3_t2 ~ i3*1
# Unique Variances
consc_curr_par1_t1 ~~ u1*consc_curr_par1_t1
consc_curr_par2_t1 ~~ u2*consc_curr_par2_t1
consc_curr_par3_t1 ~~ u3*consc_curr_par3_t1
consc_curr_par1_t2 ~~ u1*consc_curr_par1_t2
consc_curr_par2_t2 ~~ u2*consc_curr_par2_t2
consc_curr_par3_t2 ~~ u3*consc_curr_par3_t2
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_strict_consc_curr <- cfa(strict_consc_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strict_consc_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 619 19 190. 6324. 6408. 0.942 0.891 0.191 0.0279
2 weak 619 17 190. 6321. 6396. 0.942 0.913 0.170 0.0289
3 strong 619 15 192. 6319. 6385. 0.942 0.928 0.156 0.0284
4 strict 619 12 195. 6316. 6369. 0.942 0.942 0.139 0.0301# chi-square difference test for nested models
anova(fit_configural_consc_curr, fit_weak_consc_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_consc_curr 8 6324.2 6408.4 189.57
fit_weak_consc_curr 10 6320.5 6395.8 189.86 0.29498 0 2
Pr(>Chisq)
fit_configural_consc_curr
fit_weak_consc_curr 0.8629anova(fit_weak_consc_curr, fit_strong_consc_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_consc_curr 10 6320.5 6395.8 189.86
fit_strong_consc_curr 12 6319.0 6385.4 192.30 2.4372 0.018792 2
Pr(>Chisq)
fit_weak_consc_curr
fit_strong_consc_curr 0.2956anova(fit_strong_consc_curr, fit_strict_consc_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_consc_curr 12 6319.0 6385.4 192.30
fit_strict_consc_curr 15 6315.7 6368.8 195.03 2.7263 0 3
Pr(>Chisq)
fit_strong_consc_curr
fit_strict_consc_curr 0.4358Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t1 + lambda1*consc_ideal_par1_t1 + consc_ideal_par2_t1 + consc_ideal_par3_t1
consc_ideal2 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + consc_ideal_par2_t2 + consc_ideal_par3_t2
# Intercepts
consc_ideal_par1_t1 ~ i1*1
consc_ideal_par2_t1 ~ 1
consc_ideal_par3_t1 ~ 1
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ 1
consc_ideal_par3_t2 ~ 1
# Unique Variances
consc_ideal_par1_t1 ~~ consc_ideal_par1_t1
consc_ideal_par2_t1 ~~ consc_ideal_par2_t1
consc_ideal_par3_t1 ~~ consc_ideal_par3_t1
consc_ideal_par1_t2 ~~ consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ consc_ideal_par3_t2
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_configural_consc_ideal <- cfa(configural_consc_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_configural_consc_ideal, fit.measures = TRUE)
# Weak invariance model
weak_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t1 + lambda1*consc_ideal_par1_t1 + lambda2*consc_ideal_par2_t1 + lambda3*consc_ideal_par3_t1
consc_ideal2 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + lambda2*consc_ideal_par2_t2 + lambda3*consc_ideal_par3_t2
# Intercepts
consc_ideal_par1_t1 ~ i1*1
consc_ideal_par2_t1 ~ 1
consc_ideal_par3_t1 ~ 1
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ 1
consc_ideal_par3_t2 ~ 1
# Unique Variances
consc_ideal_par1_t1 ~~ consc_ideal_par1_t1
consc_ideal_par2_t1 ~~ consc_ideal_par2_t1
consc_ideal_par3_t1 ~~ consc_ideal_par3_t1
consc_ideal_par1_t2 ~~ consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ consc_ideal_par3_t2
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_weak_consc_ideal <- cfa(weak_consc_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_weak_consc_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t1 + lambda1*consc_ideal_par1_t1 + lambda2*consc_ideal_par2_t1 + lambda3*consc_ideal_par3_t1
consc_ideal2 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + lambda2*consc_ideal_par2_t2 + lambda3*consc_ideal_par3_t2
# Intercepts
consc_ideal_par1_t1 ~ i1*1
consc_ideal_par2_t1 ~ i2*1
consc_ideal_par3_t1 ~ i3*1
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ i2*1
consc_ideal_par3_t2 ~ i3*1
# Unique Variances
consc_ideal_par1_t1 ~~ consc_ideal_par1_t1
consc_ideal_par2_t1 ~~ consc_ideal_par2_t1
consc_ideal_par3_t1 ~~ consc_ideal_par3_t1
consc_ideal_par1_t2 ~~ consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ consc_ideal_par3_t2
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_strong_consc_ideal <- cfa(strong_consc_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strong_consc_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t1 + lambda1*consc_ideal_par1_t1 + lambda2*consc_ideal_par2_t1 + lambda3*consc_ideal_par3_t1
consc_ideal2 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + lambda2*consc_ideal_par2_t2 + lambda3*consc_ideal_par3_t2
# Intercepts
consc_ideal_par1_t1 ~ i1*1
consc_ideal_par2_t1 ~ i2*1
consc_ideal_par3_t1 ~ i3*1
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ i2*1
consc_ideal_par3_t2 ~ i3*1
# Unique Variances
consc_ideal_par1_t1 ~~ u1*consc_ideal_par1_t1
consc_ideal_par2_t1 ~~ u2*consc_ideal_par2_t1
consc_ideal_par3_t1 ~~ u3*consc_ideal_par3_t1
consc_ideal_par1_t2 ~~ u1*consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ u2*consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ u3*consc_ideal_par3_t2
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_strict_consc_ideal <- cfa(strict_consc_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strict_consc_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 619 19 52.9 4708. 4792. 0.970 0.944 0.0953 0.0252
2 weak 619 17 55.6 4707. 4782. 0.970 0.955 0.0858 0.0511
3 strong 619 15 55.6 4703. 4769. 0.971 0.964 0.0766 0.0513
4 strict 619 12 64.8 4706. 4759. 0.967 0.967 0.0732 0.0514# chi-square difference test for nested models
anova(fit_configural_consc_ideal, fit_weak_consc_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_consc_ideal 8 4708.1 4792.2 52.939
fit_weak_consc_ideal 10 4706.7 4782.0 55.560 2.6216 0.022407 2
Pr(>Chisq)
fit_configural_consc_ideal
fit_weak_consc_ideal 0.2696anova(fit_weak_consc_ideal, fit_strong_consc_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_consc_ideal 10 4706.7 4782.0 55.560
fit_strong_consc_ideal 12 4702.8 4769.2 55.632 0.071642 0 2
Pr(>Chisq)
fit_weak_consc_ideal
fit_strong_consc_ideal 0.9648anova(fit_strong_consc_ideal, fit_strict_consc_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_consc_ideal 12 4702.8 4769.2 55.632
fit_strict_consc_ideal 15 4705.9 4759.1 64.795 9.1628 0.057608 3
Pr(>Chisq)
fit_strong_consc_ideal
fit_strict_consc_ideal 0.0272 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t1 + lambda1*neuro_curr_par1_t1 + neuro_curr_par2_t1 + neuro_curr_par3_t1
neuro_curr2 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + neuro_curr_par2_t2 + neuro_curr_par3_t2
# Intercepts
neuro_curr_par1_t1 ~ i1*1
neuro_curr_par2_t1 ~ 1
neuro_curr_par3_t1 ~ 1
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ 1
neuro_curr_par3_t2 ~ 1
# Unique Variances
neuro_curr_par1_t1 ~~ neuro_curr_par1_t1
neuro_curr_par2_t1 ~~ neuro_curr_par2_t1
neuro_curr_par3_t1 ~~ neuro_curr_par3_t1
neuro_curr_par1_t2 ~~ neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ neuro_curr_par3_t2
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_configural_neuro_curr <- cfa(configural_neuro_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_configural_neuro_curr, fit.measures = TRUE)
# Weak invariance model
weak_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t1 + lambda1*neuro_curr_par1_t1 + lambda2*neuro_curr_par2_t1 + lambda3*neuro_curr_par3_t1
neuro_curr2 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + lambda2*neuro_curr_par2_t2 + lambda3*neuro_curr_par3_t2
# Intercepts
neuro_curr_par1_t1 ~ i1*1
neuro_curr_par2_t1 ~ 1
neuro_curr_par3_t1 ~ 1
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ 1
neuro_curr_par3_t2 ~ 1
# Unique Variances
neuro_curr_par1_t1 ~~ neuro_curr_par1_t1
neuro_curr_par2_t1 ~~ neuro_curr_par2_t1
neuro_curr_par3_t1 ~~ neuro_curr_par3_t1
neuro_curr_par1_t2 ~~ neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ neuro_curr_par3_t2
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_weak_neuro_curr <- cfa(weak_neuro_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_weak_neuro_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t1 + lambda1*neuro_curr_par1_t1 + lambda2*neuro_curr_par2_t1 + lambda3*neuro_curr_par3_t1
neuro_curr2 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + lambda2*neuro_curr_par2_t2 + lambda3*neuro_curr_par3_t2
# Intercepts
neuro_curr_par1_t1 ~ i1*1
neuro_curr_par2_t1 ~ i2*1
neuro_curr_par3_t1 ~ i3*1
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ i2*1
neuro_curr_par3_t2 ~ i3*1
# Unique Variances
neuro_curr_par1_t1 ~~ neuro_curr_par1_t1
neuro_curr_par2_t1 ~~ neuro_curr_par2_t1
neuro_curr_par3_t1 ~~ neuro_curr_par3_t1
neuro_curr_par1_t2 ~~ neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ neuro_curr_par3_t2
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_strong_neuro_curr <- cfa(strong_neuro_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strong_neuro_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t1 + lambda1*neuro_curr_par1_t1 + lambda2*neuro_curr_par2_t1 + lambda3*neuro_curr_par3_t1
neuro_curr2 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + lambda2*neuro_curr_par2_t2 + lambda3*neuro_curr_par3_t2
# Intercepts
neuro_curr_par1_t1 ~ i1*1
neuro_curr_par2_t1 ~ i2*1
neuro_curr_par3_t1 ~ i3*1
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ i2*1
neuro_curr_par3_t2 ~ i3*1
# Unique Variances
neuro_curr_par1_t1 ~~ u1*neuro_curr_par1_t1
neuro_curr_par2_t1 ~~ u2*neuro_curr_par2_t1
neuro_curr_par3_t1 ~~ u3*neuro_curr_par3_t1
neuro_curr_par1_t2 ~~ u1*neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ u2*neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ u3*neuro_curr_par3_t2
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_strict_neuro_curr <- cfa(strict_neuro_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strict_neuro_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 619 19 279. 7413. 7498. 0.906 0.823 0.234 0.0381
2 weak 619 17 280. 7410. 7486. 0.906 0.859 0.209 0.0400
3 strong 619 15 287. 7413. 7480. 0.904 0.880 0.193 0.0400
4 strict 619 12 291. 7411. 7464. 0.904 0.904 0.172 0.0449# chi-square difference test for nested models
anova(fit_configural_neuro_curr, fit_weak_neuro_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_neuro_curr 8 7413.4 7497.5 279.29
fit_weak_neuro_curr 10 7410.3 7485.5 280.15 0.86209 0 2
Pr(>Chisq)
fit_configural_neuro_curr
fit_weak_neuro_curr 0.6498anova(fit_weak_neuro_curr, fit_strong_neuro_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_neuro_curr 10 7410.3 7485.5 280.15
fit_strong_neuro_curr 12 7413.4 7479.8 287.30 7.1539 0.064522 2
Pr(>Chisq)
fit_weak_neuro_curr
fit_strong_neuro_curr 0.02796 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit_strong_neuro_curr, fit_strict_neuro_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_neuro_curr 12 7413.4 7479.8 287.30
fit_strict_neuro_curr 15 7410.9 7464.0 290.77 3.4705 0.015917 3
Pr(>Chisq)
fit_strong_neuro_curr
fit_strict_neuro_curr 0.3246Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t1 + lambda1*neuro_ideal_par1_t1 + neuro_ideal_par2_t1 + neuro_ideal_par3_t1
neuro_ideal2 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + neuro_ideal_par2_t2 + neuro_ideal_par3_t2
# Intercepts
neuro_ideal_par1_t1 ~ i1*1
neuro_ideal_par2_t1 ~ 1
neuro_ideal_par3_t1 ~ 1
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ 1
neuro_ideal_par3_t2 ~ 1
# Unique Variances
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t1
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t1
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t1
neuro_ideal_par1_t2 ~~ neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ neuro_ideal_par3_t2
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_configural_neuro_ideal <- cfa(configural_neuro_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_configural_neuro_ideal, fit.measures = TRUE)
# Weak invariance model
weak_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t1 + lambda1*neuro_ideal_par1_t1 + lambda2*neuro_ideal_par2_t1 + lambda3*neuro_ideal_par3_t1
neuro_ideal2 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + lambda2*neuro_ideal_par2_t2 + lambda3*neuro_ideal_par3_t2
# Intercepts
neuro_ideal_par1_t1 ~ i1*1
neuro_ideal_par2_t1 ~ 1
neuro_ideal_par3_t1 ~ 1
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ 1
neuro_ideal_par3_t2 ~ 1
# Unique Variances
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t1
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t1
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t1
neuro_ideal_par1_t2 ~~ neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ neuro_ideal_par3_t2
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_weak_neuro_ideal <- cfa(weak_neuro_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_weak_neuro_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t1 + lambda1*neuro_ideal_par1_t1 + lambda2*neuro_ideal_par2_t1 + lambda3*neuro_ideal_par3_t1
neuro_ideal2 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + lambda2*neuro_ideal_par2_t2 + lambda3*neuro_ideal_par3_t2
# Intercepts
neuro_ideal_par1_t1 ~ i1*1
neuro_ideal_par2_t1 ~ i2*1
neuro_ideal_par3_t1 ~ i3*1
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ i2*1
neuro_ideal_par3_t2 ~ i3*1
# Unique Variances
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t1
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t1
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t1
neuro_ideal_par1_t2 ~~ neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ neuro_ideal_par3_t2
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_strong_neuro_ideal <- cfa(strong_neuro_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strong_neuro_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t1 + lambda1*neuro_ideal_par1_t1 + lambda2*neuro_ideal_par2_t1 + lambda3*neuro_ideal_par3_t1
neuro_ideal2 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + lambda2*neuro_ideal_par2_t2 + lambda3*neuro_ideal_par3_t2
# Intercepts
neuro_ideal_par1_t1 ~ i1*1
neuro_ideal_par2_t1 ~ i2*1
neuro_ideal_par3_t1 ~ i3*1
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ i2*1
neuro_ideal_par3_t2 ~ i3*1
# Unique Variances
neuro_ideal_par1_t1 ~~ u1*neuro_ideal_par1_t1
neuro_ideal_par2_t1 ~~ u2*neuro_ideal_par2_t1
neuro_ideal_par3_t1 ~~ u3*neuro_ideal_par3_t1
neuro_ideal_par1_t2 ~~ u1*neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ u2*neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ u3*neuro_ideal_par3_t2
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_strict_neuro_ideal <- cfa(strict_neuro_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strict_neuro_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 619 19 35.1 5249. 5333. 0.980 0.963 0.0739 0.0211
2 weak 619 17 35.3 5245. 5320. 0.981 0.972 0.0639 0.0217
3 strong 619 15 36.8 5243. 5309. 0.982 0.977 0.0578 0.0216
4 strict 619 12 39.6 5239. 5293. 0.982 0.982 0.0514 0.0279# chi-square difference test for nested models
anova(fit_configural_neuro_ideal, fit_weak_neuro_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_neuro_ideal 8 5248.9 5333.0 35.062
fit_weak_neuro_ideal 10 5245.1 5320.4 35.264 0.20202 0 2
Pr(>Chisq)
fit_configural_neuro_ideal
fit_weak_neuro_ideal 0.9039anova(fit_weak_neuro_ideal, fit_strong_neuro_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_neuro_ideal 10 5245.1 5320.4 35.264
fit_strong_neuro_ideal 12 5242.6 5309.0 36.786 1.5224 0 2
Pr(>Chisq)
fit_weak_neuro_ideal
fit_strong_neuro_ideal 0.4671anova(fit_strong_neuro_ideal, fit_strict_neuro_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_neuro_ideal 12 5242.6 5309.0 36.786
fit_strict_neuro_ideal 15 5239.4 5292.5 39.574 2.788 0 3
Pr(>Chisq)
fit_strong_neuro_ideal
fit_strict_neuro_ideal 0.4255Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t1 + lambda1*openn_curr_par1_t1 + openn_curr_par2_t1 + openn_curr_par3_t1
openn_curr2 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + openn_curr_par2_t2 + openn_curr_par3_t2
# Intercepts
openn_curr_par1_t1 ~ i1*1
openn_curr_par2_t1 ~ 1
openn_curr_par3_t1 ~ 1
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ 1
openn_curr_par3_t2 ~ 1
# Unique Variances
openn_curr_par1_t1 ~~ openn_curr_par1_t1
openn_curr_par2_t1 ~~ openn_curr_par2_t1
openn_curr_par3_t1 ~~ openn_curr_par3_t1
openn_curr_par1_t2 ~~ openn_curr_par1_t2
openn_curr_par2_t2 ~~ openn_curr_par2_t2
openn_curr_par3_t2 ~~ openn_curr_par3_t2
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_configural_openn_curr <- cfa(configural_openn_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_configural_openn_curr, fit.measures = TRUE)
# Weak invariance model
weak_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t1 + lambda1*openn_curr_par1_t1 + lambda2*openn_curr_par2_t1 + lambda3*openn_curr_par3_t1
openn_curr2 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + lambda2*openn_curr_par2_t2 + lambda3*openn_curr_par3_t2
# Intercepts
openn_curr_par1_t1 ~ i1*1
openn_curr_par2_t1 ~ 1
openn_curr_par3_t1 ~ 1
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ 1
openn_curr_par3_t2 ~ 1
# Unique Variances
openn_curr_par1_t1 ~~ openn_curr_par1_t1
openn_curr_par2_t1 ~~ openn_curr_par2_t1
openn_curr_par3_t1 ~~ openn_curr_par3_t1
openn_curr_par1_t2 ~~ openn_curr_par1_t2
openn_curr_par2_t2 ~~ openn_curr_par2_t2
openn_curr_par3_t2 ~~ openn_curr_par3_t2
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_weak_openn_curr <- cfa(weak_openn_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_weak_openn_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t1 + lambda1*openn_curr_par1_t1 + lambda2*openn_curr_par2_t1 + lambda3*openn_curr_par3_t1
openn_curr2 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + lambda2*openn_curr_par2_t2 + lambda3*openn_curr_par3_t2
# Intercepts
openn_curr_par1_t1 ~ i1*1
openn_curr_par2_t1 ~ i2*1
openn_curr_par3_t1 ~ i3*1
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ i2*1
openn_curr_par3_t2 ~ i3*1
# Unique Variances
openn_curr_par1_t1 ~~ openn_curr_par1_t1
openn_curr_par2_t1 ~~ openn_curr_par2_t1
openn_curr_par3_t1 ~~ openn_curr_par3_t1
openn_curr_par1_t2 ~~ openn_curr_par1_t2
openn_curr_par2_t2 ~~ openn_curr_par2_t2
openn_curr_par3_t2 ~~ openn_curr_par3_t2
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_strong_openn_curr <- cfa(strong_openn_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strong_openn_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t1 + lambda1*openn_curr_par1_t1 + lambda2*openn_curr_par2_t1 + lambda3*openn_curr_par3_t1
openn_curr2 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + lambda2*openn_curr_par2_t2 + lambda3*openn_curr_par3_t2
# Intercepts
openn_curr_par1_t1 ~ i1*1
openn_curr_par2_t1 ~ i2*1
openn_curr_par3_t1 ~ i3*1
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ i2*1
openn_curr_par3_t2 ~ i3*1
# Unique Variances
openn_curr_par1_t1 ~~ u1*openn_curr_par1_t1
openn_curr_par2_t1 ~~ u2*openn_curr_par2_t1
openn_curr_par3_t1 ~~ u3*openn_curr_par3_t1
openn_curr_par1_t2 ~~ u1*openn_curr_par1_t2
openn_curr_par2_t2 ~~ u2*openn_curr_par2_t2
openn_curr_par3_t2 ~~ u3*openn_curr_par3_t2
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_strict_openn_curr <- cfa(strict_openn_curr, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strict_openn_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 619 19 174. 6237. 6321. 0.916 0.842 0.183 0.0423
2 weak 619 17 175. 6234. 6309. 0.916 0.874 0.163 0.0452
3 strong 619 15 175. 6230. 6296. 0.917 0.897 0.148 0.0456
4 strict 619 12 183. 6232. 6285. 0.915 0.915 0.134 0.0535# chi-square difference test for nested models
anova(fit_configural_openn_curr, fit_weak_openn_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_openn_curr 8 6237.1 6321.2 174.27
fit_weak_openn_curr 10 6233.9 6309.1 175.05 0.77456 0 2
Pr(>Chisq)
fit_configural_openn_curr
fit_weak_openn_curr 0.6789anova(fit_weak_openn_curr, fit_strong_openn_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_openn_curr 10 6233.9 6309.1 175.05
fit_strong_openn_curr 12 6229.9 6296.4 175.14 0.095301 0 2
Pr(>Chisq)
fit_weak_openn_curr
fit_strong_openn_curr 0.9535anova(fit_strong_openn_curr, fit_strict_openn_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_openn_curr 12 6229.9 6296.4 175.14
fit_strict_openn_curr 15 6231.6 6284.7 182.76 7.6164 0.049859 3
Pr(>Chisq)
fit_strong_openn_curr
fit_strict_openn_curr 0.05464 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t1 + lambda1*openn_ideal_par1_t1 + openn_ideal_par2_t1 + openn_ideal_par3_t1
openn_ideal2 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + openn_ideal_par2_t2 + openn_ideal_par3_t2
# Intercepts
openn_ideal_par1_t1 ~ i1*1
openn_ideal_par2_t1 ~ 1
openn_ideal_par3_t1 ~ 1
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ 1
openn_ideal_par3_t2 ~ 1
# Unique Variances
openn_ideal_par1_t1 ~~ openn_ideal_par1_t1
openn_ideal_par2_t1 ~~ openn_ideal_par2_t1
openn_ideal_par3_t1 ~~ openn_ideal_par3_t1
openn_ideal_par1_t2 ~~ openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ openn_ideal_par3_t2
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_configural_openn_ideal <- cfa(configural_openn_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_configural_openn_ideal, fit.measures = TRUE)
# Weak invariance model
weak_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t1 + lambda1*openn_ideal_par1_t1 + lambda2*openn_ideal_par2_t1 + lambda3*openn_ideal_par3_t1
openn_ideal2 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + lambda2*openn_ideal_par2_t2 + lambda3*openn_ideal_par3_t2
# Intercepts
openn_ideal_par1_t1 ~ i1*1
openn_ideal_par2_t1 ~ 1
openn_ideal_par3_t1 ~ 1
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ 1
openn_ideal_par3_t2 ~ 1
# Unique Variances
openn_ideal_par1_t1 ~~ openn_ideal_par1_t1
openn_ideal_par2_t1 ~~ openn_ideal_par2_t1
openn_ideal_par3_t1 ~~ openn_ideal_par3_t1
openn_ideal_par1_t2 ~~ openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ openn_ideal_par3_t2
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_weak_openn_ideal <- cfa(weak_openn_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_weak_openn_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t1 + lambda1*openn_ideal_par1_t1 + lambda2*openn_ideal_par2_t1 + lambda3*openn_ideal_par3_t1
openn_ideal2 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + lambda2*openn_ideal_par2_t2 + lambda3*openn_ideal_par3_t2
# Intercepts
openn_ideal_par1_t1 ~ i1*1
openn_ideal_par2_t1 ~ i2*1
openn_ideal_par3_t1 ~ i3*1
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ i2*1
openn_ideal_par3_t2 ~ i3*1
# Unique Variances
openn_ideal_par1_t1 ~~ openn_ideal_par1_t1
openn_ideal_par2_t1 ~~ openn_ideal_par2_t1
openn_ideal_par3_t1 ~~ openn_ideal_par3_t1
openn_ideal_par1_t2 ~~ openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ openn_ideal_par3_t2
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_strong_openn_ideal <- cfa(strong_openn_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strong_openn_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t1 + lambda1*openn_ideal_par1_t1 + lambda2*openn_ideal_par2_t1 + lambda3*openn_ideal_par3_t1
openn_ideal2 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + lambda2*openn_ideal_par2_t2 + lambda3*openn_ideal_par3_t2
# Intercepts
openn_ideal_par1_t1 ~ i1*1
openn_ideal_par2_t1 ~ i2*1
openn_ideal_par3_t1 ~ i3*1
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ i2*1
openn_ideal_par3_t2 ~ i3*1
# Unique Variances
openn_ideal_par1_t1 ~~ u1*openn_ideal_par1_t1
openn_ideal_par2_t1 ~~ u2*openn_ideal_par2_t1
openn_ideal_par3_t1 ~~ u3*openn_ideal_par3_t1
openn_ideal_par1_t2 ~~ u1*openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ u2*openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ u3*openn_ideal_par3_t2
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_strict_openn_ideal <- cfa(strict_openn_ideal, data = df_sbsa_wide_pers, mimic = "mplus")
summary(fit_strict_openn_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 619 19 111. 5753. 5837. 0.925 0.859 0.144 0.0425
2 weak 619 17 112. 5750. 5825. 0.925 0.888 0.129 0.0507
3 strong 619 15 115. 5749. 5815. 0.925 0.906 0.118 0.0520
4 strict 619 12 121. 5749. 5802. 0.923 0.923 0.107 0.0552# chi-square difference test for nested models
anova(fit_configural_openn_ideal, fit_weak_openn_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_openn_ideal 8 5752.8 5836.9 111.05
fit_weak_openn_ideal 10 5750.2 5825.4 112.42 1.3684 0 2
Pr(>Chisq)
fit_configural_openn_ideal
fit_weak_openn_ideal 0.5045anova(fit_weak_openn_ideal, fit_strong_openn_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_openn_ideal 10 5750.2 5825.4 112.42
fit_strong_openn_ideal 12 5748.9 5815.3 115.12 2.7037 0.023841 2
Pr(>Chisq)
fit_weak_openn_ideal
fit_strong_openn_ideal 0.2588anova(fit_strong_openn_ideal, fit_strict_openn_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_openn_ideal 12 5748.9 5815.3 115.12
fit_strict_openn_ideal 15 5748.7 5801.8 120.95 5.8286 0.039028 3
Pr(>Chisq)
fit_strong_openn_ideal
fit_strict_openn_ideal 0.1203Summary stats:
table(df_sbsa$sb06_01) # How much do you want to change your personality in general?
1 2 3 4 5
22 54 130 77 22 summary(df_sbsa$sb06_01) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.000 3.000 3.000 3.075 4.000 5.000 922 table(df_sbsa$sa06_01) # How much do you want to be better at accepting yourself for who you are?
1 2 3 4 5
6 11 42 98 138 summary(df_sbsa$sa06_01) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.00 4.00 4.00 4.19 5.00 5.00 932 Plot:
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb06_01)), aes(x=sb06_01)) + geom_histogram(bins=5) +
labs(x="General change goal", y="Frequency") + theme_bw(),
ggplot(df_sbsa %>% filter(!is.na(sa06_01)), aes(x=sa06_01)) + geom_histogram(bins=5) +
labs(x="General acceptance goal", y="Frequency") + theme_bw(),
ncol = 2
)
Skill Building
Pre:
Here is a list of 15 personality traits. You might be high or low on any of these traits. Regardless of how high or low you are on these traits, we want to know how much you [want to] change. Please rate how much you would like to change your level of each of these traits, where 1 = I do not want to change and 5 = I want to change a lot.
Post:
Here is a list of 15 personality traits. During the study, you may have tried to change in some of these traits. We want to know how you think you changed in each trait. Please rate how much you changed your level of each of these traits during this study, where 1 = I am completely the same and 5 = I have changed a lot.
Self Acceptance
Pre:
Here is a list of 15 personality traits. You might be high or low on any of these traits. Regardless of how high or low you are on these traits, we want to know how much you accept yourself. Please rate how much you accept your level of each of these traits, where 1 = I completely accept myself and 5 = I want to accept myself more.
Post:
Here is a list of 15 personality traits. During the study, you may have tried to accept your level on some of these traits more. We want to know how much you think you accept yourself more because of this study. Please rate how much you accept your level of each of these traits more during this study, where 1 = I accept myself the same and 5 = I accept this about myself much more.
Plot distributions
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 01], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_01)), aes(x=sb07_01)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_01)), aes(x=sb12_01)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_01)), aes(x=sa07_01)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_01)), aes(x=sa14_01)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 02], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_02)), aes(x=sb07_02)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_02)), aes(x=sb12_02)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_02)), aes(x=sa07_02)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_02)), aes(x=sa14_02)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 03], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_03)), aes(x=sb07_03)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_03)), aes(x=sb12_03)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_03)), aes(x=sa07_03)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_03)), aes(x=sa14_03)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 04], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_04)), aes(x=sb07_04)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_04)), aes(x=sb12_04)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_04)), aes(x=sa07_04)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_04)), aes(x=sa14_04)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 05], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_05)), aes(x=sb07_05)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_05)), aes(x=sb12_05)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_05)), aes(x=sa07_05)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_05)), aes(x=sa14_05)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 06], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_06)), aes(x=sb07_06)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_06)), aes(x=sb12_06)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_06)), aes(x=sa07_06)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_06)), aes(x=sa14_06)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 07], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_07)), aes(x=sb07_07)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_07)), aes(x=sb12_07)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_07)), aes(x=sa07_07)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_07)), aes(x=sa14_07)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 08], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_08)), aes(x=sb07_08)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_08)), aes(x=sb12_08)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_08)), aes(x=sa07_08)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_08)), aes(x=sa14_08)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 09], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_09)), aes(x=sb07_09)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_09)), aes(x=sb12_09)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_09)), aes(x=sa07_09)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_09)), aes(x=sa14_09)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 10], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_10)), aes(x=sb07_10)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_10)), aes(x=sb12_10)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_10)), aes(x=sa07_10)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_10)), aes(x=sa14_10)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 11], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_11)), aes(x=sb07_11)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_11)), aes(x=sb12_11)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_11)), aes(x=sa07_11)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_11)), aes(x=sa14_11)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 12], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_12)), aes(x=sb07_12)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_12)), aes(x=sb12_12)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_12)), aes(x=sa07_12)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_12)), aes(x=sa14_12)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 13], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_13)), aes(x=sb07_13)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_13)), aes(x=sb12_13)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_13)), aes(x=sa07_13)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_13)), aes(x=sa14_13)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 14], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_14)), aes(x=sb07_14)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_14)), aes(x=sb12_14)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_14)), aes(x=sa07_14)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_14)), aes(x=sa14_14)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 15], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa %>% filter(!is.na(sb07_15)), aes(x=sb07_15)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sb12_15)), aes(x=sb12_15)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa07_15)), aes(x=sa07_15)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa %>% filter(!is.na(sa14_15)), aes(x=sa14_15)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
All four psychological well-being indicators will be positively correlated with a greater similarity between current- and ideal self-ratings of personality.
To examine this at the level of overall profiles, we will compute the correlations between the psychological well-being indicators and the Fisher z transformed correlations between the facet- and item-level real-ideal self-profiles. To examine this at the level of individual traits, we will compute the correlation between psychological well-being indicators and the squared difference between current- and ideal-self rating for each Big Five trait and facet.
Computations:
cormat_profile <- cor(df_sbsa[, c("swls", "meaning", "selfes", "concept",
"profile_corr_item_z", "profile_corr_facet_z")], use = "pairwise.complete.obs")
rownames(cormat_profile) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
"Item-Level Profile Corr (z)", "Facet-Level Profile Corr (z)")
colnames(cormat_profile) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
"Item-Level Profile Corr (z)", "Facet-Level Profile Corr (z)")corrplot(cormat_profile, type = "lower", order = "original", tl.col = "black", tl.srt = 10,
addCoef.col ='black', number.cex = 0.7, diag = FALSE) # also add numbers
Computations:
cormat_sqtraits <- cor(df_sbsa[, c("swls", "meaning", "selfes", "concept",
paste0(str_trunc(names(b5_vars)[1:5], 5, ellipsis = ""), "_sqdiff"))],
use = "pairwise.complete.obs")
rownames(cormat_sqtraits) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
str_to_title(names(b5_vars)[1:5]))
colnames(cormat_sqtraits) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
str_to_title(names(b5_vars)[1:5]))
cormat_sqfacets <- cor(df_sbsa[, c("swls", "meaning", "selfes", "concept",
paste0(str_trunc(names(b5_vars)[6:20], 5, ellipsis = ""), "_sqdiff"))],
use = "pairwise.complete.obs")
rownames(cormat_sqfacets) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
str_to_title(names(b5_vars)[6:20]))
colnames(cormat_sqfacets) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
str_to_title(names(b5_vars)[6:20]))Big Five traits
corrplot(cormat_sqtraits, type = "lower", order = "original", tl.col = "black", tl.srt = 10,
addCoef.col ='black', number.cex = 0.7, diag = FALSE) # also add numbers
Big Five facets
corrplot(cormat_sqfacets, type = "lower", order = "original", tl.col = "black", tl.srt = 10,
addCoef.col ='black', number.cex = 0.6, diag = FALSE) # also add numbers
Both groups will increase in all four psychological well-being indicators.
We will test the mean-level difference between baseline and follow up using a latent change model.
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_swls_hyp2 <- '
swls_t1 =~ 1*sw06_01_t1 + lamb2*sw06_02_t1 + lamb3*sw06_03_t1 + lamb4*sw06_04_t1 # This specifies the measurement model for swls_t1
swls_t2 =~ 1*sw06_01_t2 + lamb2*sw06_02_t2 + lamb3*sw06_03_t2 + lamb4*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadings
swls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1
d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2
swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0
swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0
d_swls_1 ~ 1 # This estimates the intercept of the change score
swls_t1 ~ 1 # This estimates the intercept of swls_t1
d_swls_1 ~~ d_swls_1 # This estimates the variance of the change scores
swls_t1 ~~ swls_t1 # This estimates the variance of the swls_t1
d_swls_1 ~ swls_t1 # This estimates the self-feedback parameter
sw06_01_t1 ~~ sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2
sw06_02_t1 ~~ sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2
sw06_03_t1 ~~ sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2
sw06_04_t1 ~~ sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2
sw06_01_t1 ~~ res1*sw06_01_t1 # This allows residual variance on indicator X1 at T1
sw06_02_t1 ~~ res2*sw06_02_t1 # This allows residual variance on indicator X2 at T1
sw06_03_t1 ~~ res3*sw06_03_t1 # This allows residual variance on indicator X3 at T1
sw06_04_t1 ~~ res4*sw06_04_t1 # This allows residual variance on indicator X4 at T1
sw06_01_t2 ~~ res1*sw06_01_t2 # This allows residual variance on indicator X1 at T2
sw06_02_t2 ~~ res2*sw06_02_t2 # This allows residual variance on indicator X2 at T2
sw06_03_t2 ~~ res3*sw06_03_t2 # This allows residual variance on indicator X3 at T2
sw06_04_t2 ~~ res4*sw06_04_t2 # This allows residual variance on indicator X4 at T2
sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
sw06_02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
sw06_03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
sw06_04_t1 ~ m4*1 # This estimates the intercept of X4 at T1
sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
sw06_02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
sw06_03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sw06_04_t2 ~ m4*1 # This estimates the intercept of X4 at T2
'
fit_mi_lcs_swls_hyp2 <- lavaan(mi_lcs_swls_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_swls_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_swls_hyp2) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 618 | 19 | 12571.75 | 12655.86 | 0.989 | 0.046 | 0.029 |
# parameters of interest
params_lcs_swls_hyp2 <- broom::tidy(fit_mi_lcs_swls_hyp2, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("swls_t2 ~ swls_t1", "d_swls_1 =~ swls_t2", "d_swls_1 ~ swls_t1", # change parameters
"d_swls_1 ~1 ", "swls_t1 ~1 ", "", # means
"d_swls_1 ~~ d_swls_1")) # variances
kable(params_lcs_swls_hyp2, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| swls_t2 ~ swls_t1 | 1.000 | 1.000 | 1.000 | 0.946 | NA | NA |
| d_swls_1 =~ swls_t2 | 1.000 | 1.000 | 1.000 | 0.635 | NA | NA |
| d_swls_1 ~1 | 0.761 | 0.565 | 0.956 | 1.178 | 7.636 | 0 |
| swls_t1 ~1 | 2.701 | 2.612 | 2.790 | 2.807 | 59.290 | 0 |
| d_swls_1 ~~ d_swls_1 | 0.391 | 0.313 | 0.470 | 0.938 | 9.763 | 0 |
| d_swls_1 ~ swls_t1 | -0.167 | -0.229 | -0.105 | -0.249 | -5.249 | 0 |
Plot model:
semPaths(fit_mi_lcs_swls_hyp2, what="est",
sizeLat = 7, sizeMan = 7, edge.label.cex = .75, intercepts = T, edge.color="black")
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_meaning_hyp2 <- '
meaning_t1 =~ 1*meaning_par1_t1 + lamb2*meaning_par2_t1 + lamb3*meaning_par3_t1 # This specifies the measurement model for meaning_t1
meaning_t2 =~ 1*meaning_par1_t2 + lamb2*meaning_par2_t2 + lamb3*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadings
meaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1
d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2
meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0
meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0
d_meaning_1 ~ 1 # This estimates the intercept of the change score
meaning_t1 ~ 1 # This estimates the intercept of meaning_t1
d_meaning_1 ~~ d_meaning_1 # This estimates the variance of the change scores
meaning_t1 ~~ meaning_t1 # This estimates the variance of the meaning_t1
d_meaning_1 ~ meaning_t1 # This estimates the self-feedback parameter
meaning_par1_t1 ~~ meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
meaning_par2_t1 ~~ meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
meaning_par3_t1 ~~ meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
meaning_par1_t1 ~~ res1*meaning_par1_t1 # This allows residual variance on indicator X1 at T1
meaning_par2_t1 ~~ res2*meaning_par2_t1 # This allows residual variance on indicator X2 at T1
meaning_par3_t1 ~~ res3*meaning_par3_t1 # This allows residual variance on indicator X3 at T1
meaning_par1_t2 ~~ res1*meaning_par1_t2 # This allows residual variance on indicator X1 at T2
meaning_par2_t2 ~~ res2*meaning_par2_t2 # This allows residual variance on indicator X2 at T2
meaning_par3_t2 ~~ res3*meaning_par3_t2 # This allows residual variance on indicator X3 at T2
meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
meaning_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
meaning_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
meaning_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
meaning_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
'
fit_mi_lcs_meaning_hyp2 <- lavaan(mi_lcs_meaning_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_meaning_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_meaning_hyp2) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 618 | 15 | 9254.729 | 9321.126 | 0.996 | 0.037 | 0.026 |
# parameters of interest
params_lcs_meaning_hyp2 <- broom::tidy(fit_mi_lcs_meaning_hyp2, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("meaning_t2 ~ meaning_t1", "d_meaning_1 =~ meaning_t2", "d_meaning_1 ~ meaning_t1", # change parameters
"d_meaning_1 ~1 ", "meaning_t1 ~1 ", "", # means
"d_meaning_1 ~~ d_meaning_1")) # variances
kable(params_lcs_meaning_hyp2, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| meaning_t2 ~ meaning_t1 | 1.000 | 1.000 | 1.000 | 0.966 | NA | NA |
| d_meaning_1 =~ meaning_t2 | 1.000 | 1.000 | 1.000 | 0.825 | NA | NA |
| d_meaning_1 ~1 | 1.352 | 1.088 | 1.615 | 1.667 | 10.068 | 0 |
| meaning_t1 ~1 | 3.230 | 3.140 | 3.321 | 3.403 | 69.859 | 0 |
| d_meaning_1 ~~ d_meaning_1 | 0.560 | 0.440 | 0.680 | 0.852 | 9.161 | 0 |
| d_meaning_1 ~ meaning_t1 | -0.329 | -0.405 | -0.253 | -0.385 | -8.468 | 0 |
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_selfes_hyp2 <- '
selfes_t1 =~ 1*selfes_par1_t1 + lamb2*selfes_par2_t1 + lamb3*selfes_par3_t1 # This specifies the measurement model for selfes_t1
selfes_t2 =~ 1*selfes_par1_t2 + lamb2*selfes_par2_t2 + lamb3*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadings
selfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1
d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2
selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0
selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0
d_selfes_1 ~ 1 # This estimates the intercept of the change score
selfes_t1 ~ 1 # This estimates the intercept of selfes_t1
d_selfes_1 ~~ d_selfes_1 # This estimates the variance of the change scores
selfes_t1 ~~ selfes_t1 # This estimates the variance of the selfes_t1
d_selfes_1 ~ selfes_t1 # This estimates the self-feedback parameter
selfes_par1_t1 ~~ selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
selfes_par2_t1 ~~ selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
selfes_par3_t1 ~~ selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
selfes_par1_t1 ~~ res1*selfes_par1_t1 # This allows residual variance on indicator X1 at T1
selfes_par2_t1 ~~ res2*selfes_par2_t1 # This allows residual variance on indicator X2 at T1
selfes_par3_t1 ~~ res3*selfes_par3_t1 # This allows residual variance on indicator X3 at T1
selfes_par1_t2 ~~ res1*selfes_par1_t2 # This allows residual variance on indicator X1 at T2
selfes_par2_t2 ~~ res2*selfes_par2_t2 # This allows residual variance on indicator X2 at T2
selfes_par3_t2 ~~ res3*selfes_par3_t2 # This allows residual variance on indicator X3 at T2
selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
selfes_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
selfes_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
selfes_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
selfes_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
'
fit_mi_lcs_selfes_hyp2 <- lavaan(mi_lcs_selfes_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_selfes_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_selfes_hyp2) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 618 | 15 | 6481.653 | 6548.05 | 1 | 0 | 0.01 |
# parameters of interest
params_lcs_selfes_hyp2 <- broom::tidy(fit_mi_lcs_selfes_hyp2, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("selfes_t2 ~ selfes_t1", "d_selfes_1 =~ selfes_t2", "d_selfes_1 ~ selfes_t1", # change parameters
"d_selfes_1 ~1 ", "selfes_t1 ~1 ", "", # means
"d_selfes_1 ~~ d_selfes_1")) # variances
kable(params_lcs_selfes_hyp2, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| selfes_t2 ~ selfes_t1 | 1.000 | 1.000 | 1.000 | 1.002 | NA | NA |
| d_selfes_1 =~ selfes_t2 | 1.000 | 1.000 | 1.000 | 0.684 | NA | NA |
| d_selfes_1 ~1 | 1.027 | 0.817 | 1.237 | 1.930 | 9.594 | 0 |
| selfes_t1 ~1 | 3.131 | 3.062 | 3.200 | 4.019 | 89.000 | 0 |
| d_selfes_1 ~~ d_selfes_1 | 0.250 | 0.204 | 0.295 | 0.881 | 10.763 | 0 |
| d_selfes_1 ~ selfes_t1 | -0.235 | -0.296 | -0.175 | -0.345 | -7.632 | 0 |
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_concept_hyp2 <- '
concept_t1 =~ 1*concept_par1_t1 + lamb2*concept_par2_t1 + lamb3*concept_par3_t1 # This specifies the measurement model for concept_t1
concept_t2 =~ 1*concept_par1_t2 + lamb2*concept_par2_t2 + lamb3*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadings
concept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1
d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2
concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0
concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0
d_concept_1 ~ 1 # This estimates the intercept of the change score
concept_t1 ~ 1 # This estimates the intercept of concept_t1
d_concept_1 ~~ d_concept_1 # This estimates the variance of the change scores
concept_t1 ~~ concept_t1 # This estimates the variance of the concept_t1
d_concept_1 ~ concept_t1 # This estimates the self-feedback parameter
concept_par1_t1 ~~ concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
concept_par2_t1 ~~ concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
concept_par3_t1 ~~ concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
concept_par1_t1 ~~ res1*concept_par1_t1 # This allows residual variance on indicator X1 at T1
concept_par2_t1 ~~ res2*concept_par2_t1 # This allows residual variance on indicator X2 at T1
concept_par3_t1 ~~ res3*concept_par3_t1 # This allows residual variance on indicator X3 at T1
concept_par1_t2 ~~ res1*concept_par1_t2 # This allows residual variance on indicator X1 at T2
concept_par2_t2 ~~ res2*concept_par2_t2 # This allows residual variance on indicator X2 at T2
concept_par3_t2 ~~ res3*concept_par3_t2 # This allows residual variance on indicator X3 at T2
concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
concept_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
concept_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
concept_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
concept_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
'
fit_mi_lcs_concept_hyp2 <- lavaan(mi_lcs_concept_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_concept_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_concept_hyp2) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 618 | 15 | 7524.884 | 7591.282 | 1 | 0.011 | 0.017 |
# parameters of interest
params_lcs_concept_hyp2 <- broom::tidy(fit_mi_lcs_concept_hyp2, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("concept_t2 ~ concept_t1", "d_concept_1 =~ concept_t2", "d_concept_1 ~ concept_t1", # change parameters
"d_concept_1 ~1 ", "concept_t1 ~1 ", "", # means
"d_concept_1 ~~ d_concept_1")) # variances
kable(params_lcs_concept_hyp2, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| concept_t2 ~ concept_t1 | 1.000 | 1.000 | 1.000 | 0.969 | NA | NA |
| d_concept_1 =~ concept_t2 | 1.000 | 1.000 | 1.000 | 0.797 | NA | NA |
| d_concept_1 ~1 | 1.102 | 0.888 | 1.316 | 1.948 | 10.110 | 0 |
| concept_t1 ~1 | 2.925 | 2.860 | 2.989 | 4.255 | 89.037 | 0 |
| d_concept_1 ~~ d_concept_1 | 0.276 | 0.222 | 0.330 | 0.862 | 10.030 | 0 |
| d_concept_1 ~ concept_t1 | -0.306 | -0.374 | -0.237 | -0.371 | -8.746 | 0 |
The distance between ideal- and current-self will decrease in both groups.
We will use two strategies to test this hypothesis. First, we will compute the Fisher z-transformed profile correlation between current- and ideal-self and test whether it increased across assessments. Second, we will test whether the squared difference between current- and ideal-self ratings for each Big Five trait decreased across assessments. We will test mean-level differences in profile correlations and squared differences between baseline and follow up using repeated-measures t-test.
Reshape to wide:
# reshape to wide
df_sbsa_wide_profdiff <- df_sbsa %>%
arrange(pid, time) %>%
select(pid, time, profile_corr_item_z, profile_corr_facet_z, ends_with("_sqdiff")) %>%
pivot_wider(names_from = time,
names_sep = "_t",
values_from = -c(pid, time))Checking assumptions
# item-level profile correlation
shapiro.test(df_sbsa_wide_profdiff$profile_corr_item_z_t2 - df_sbsa_wide_profdiff$profile_corr_item_z_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$profile_corr_item_z_t2 - df_sbsa_wide_profdiff$profile_corr_item_z_t1
W = 0.90485, p-value < 2.2e-16lillie.test(df_sbsa_wide_profdiff$profile_corr_item_z_t2 - df_sbsa_wide_profdiff$profile_corr_item_z_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$profile_corr_item_z_t2 - df_sbsa_wide_profdiff$profile_corr_item_z_t1
D = 0.092715, p-value = 1.769e-13# descriptives
psych::describe(df_sbsa_wide_profdiff$profile_corr_item_z_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 609 0.38 0.48 0.37 0.36 0.46 -1.05 2.65 3.7 0.48 1.07 0.02psych::describe(df_sbsa_wide_profdiff$profile_corr_item_z_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 615 0.55 0.53 0.51 0.53 0.52 -0.9 3.44 4.34 0.72 2.44 0.02# facet-level profile correlation
shapiro.test(df_sbsa_wide_profdiff$profile_corr_facet_z_t2 - df_sbsa_wide_profdiff$profile_corr_facet_z_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$profile_corr_facet_z_t2 - df_sbsa_wide_profdiff$profile_corr_facet_z_t1
W = 0.9228, p-value < 2.2e-16lillie.test(df_sbsa_wide_profdiff$profile_corr_facet_z_t2 - df_sbsa_wide_profdiff$profile_corr_facet_z_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$profile_corr_facet_z_t2 - df_sbsa_wide_profdiff$profile_corr_facet_z_t1
D = 0.086666, p-value = 1.089e-11# descriptives
psych::describe(df_sbsa_wide_profdiff$profile_corr_facet_z_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 609 0.28 0.66 0.22 0.25 0.65 -1.5 2.65 4.15 0.46 0.42 0.03psych::describe(df_sbsa_wide_profdiff$profile_corr_facet_z_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 615 0.5 0.73 0.46 0.48 0.74 -1.22 3.94 5.16 0.47 0.82 0.03Results summary:
t_profile_corr_item <- t.test(df_sbsa_wide_profdiff$profile_corr_item_z_t2, df_sbsa_wide_profdiff$profile_corr_item_z_t1, paired = TRUE)
t_profile_corr_facet <- t.test(df_sbsa_wide_profdiff$profile_corr_facet_z_t2, df_sbsa_wide_profdiff$profile_corr_facet_z_t1, paired = TRUE)
kable(bind_rows(tidy(t_profile_corr_item), tidy(t_profile_corr_facet)) %>% mutate(outcome = c("Item-level profile corr.", "Facet-level profile corr.")) %>% select(outcome, everything()), digits = 3)| outcome | estimate | statistic | p.value | parameter | conf.low | conf.high | method | alternative |
|---|---|---|---|---|---|---|---|---|
| Item-level profile corr. | 0.172 | 10.573 | 0 | 604 | 0.140 | 0.204 | Paired t-test | two.sided |
| Facet-level profile corr. | 0.216 | 9.865 | 0 | 604 | 0.173 | 0.259 | Paired t-test | two.sided |
# effect sizes
d_profile_corr_item <- psych::cohen.d(profile_corr_item_z ~ time, data = df_sbsa)
d_profile_corr_item$cohen.d lower effect upper
profile_corr_item_z 0.2350445 0.3480071 0.4608281d_profile_corr_facet <- psych::cohen.d(profile_corr_facet_z ~ time, data = df_sbsa)
d_profile_corr_facet$cohen.d lower effect upper
profile_corr_facet_z 0.2061032 0.3189235 0.4316158Plot
profile_df_plot <- df_sbsa %>% select(pid, time, profile_corr_item_z, profile_corr_facet_z) %>%
pivot_longer(-c(pid, time), names_to = "itemfacet", values_to = "corr") %>%
mutate(itemfacet2 = fct_recode(itemfacet, "Item-level" = "profile_corr_item_z", "Facet-level" = "profile_corr_facet_z"),
itemfacet2 = fct_reorder(itemfacet2, corr, .desc = F))
ggplot(profile_df_plot) +
aes(x = as.factor(time), y = corr) +
geom_boxplot() +
geom_violin(fill = NA) +
facet_wrap(vars(itemfacet2)) +
labs(x = "Measurement Occasion", y = "Profile correlation",
title = "H3: Distance between ideal- and current-self") +
theme_bw()
Checking assumptions
# extra
shapiro.test(df_sbsa_wide_profdiff$extra_sqdiff_t2 - df_sbsa_wide_profdiff$extra_sqdiff_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$extra_sqdiff_t2 - df_sbsa_wide_profdiff$extra_sqdiff_t1
W = 0.87633, p-value < 2.2e-16lillie.test(df_sbsa_wide_profdiff$extra_sqdiff_t2 - df_sbsa_wide_profdiff$extra_sqdiff_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$extra_sqdiff_t2 - df_sbsa_wide_profdiff$extra_sqdiff_t1
D = 0.14993, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa_wide_profdiff$extra_sqdiff_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 609 1.84 2.03 1.17 1.47 1.48 0 11.11 11.11 1.76 3.3 0.08psych::describe(df_sbsa_wide_profdiff$extra_sqdiff_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 615 1.34 1.62 0.84 1.02 0.99 0 10.56 10.56 2.04 4.75 0.07# agree
shapiro.test(df_sbsa_wide_profdiff$agree_sqdiff_t2 - df_sbsa_wide_profdiff$agree_sqdiff_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$agree_sqdiff_t2 - df_sbsa_wide_profdiff$agree_sqdiff_t1
W = 0.79415, p-value < 2.2e-16lillie.test(df_sbsa_wide_profdiff$agree_sqdiff_t2 - df_sbsa_wide_profdiff$agree_sqdiff_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$agree_sqdiff_t2 - df_sbsa_wide_profdiff$agree_sqdiff_t1
D = 0.19411, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa_wide_profdiff$agree_sqdiff_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 609 0.55 0.9 0.17 0.35 0.25 0 7.11 7.11 3.46 15.86 0.04psych::describe(df_sbsa_wide_profdiff$agree_sqdiff_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 615 0.39 0.74 0.11 0.23 0.15 0 10.03 10.03 5.94 57.41 0.03# consc
shapiro.test(df_sbsa_wide_profdiff$consc_sqdiff_t2 - df_sbsa_wide_profdiff$consc_sqdiff_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$consc_sqdiff_t2 - df_sbsa_wide_profdiff$consc_sqdiff_t1
W = 0.82623, p-value < 2.2e-16lillie.test(df_sbsa_wide_profdiff$consc_sqdiff_t2 - df_sbsa_wide_profdiff$consc_sqdiff_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$consc_sqdiff_t2 - df_sbsa_wide_profdiff$consc_sqdiff_t1
D = 0.15318, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa_wide_profdiff$consc_sqdiff_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 609 1.71 2.1 1 1.31 1.32 0 14.06 14.06 2.25 6.86 0.09psych::describe(df_sbsa_wide_profdiff$consc_sqdiff_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 615 1.36 1.74 0.69 1.01 0.99 0 13.44 13.44 2.4 7.99 0.07# neuro
shapiro.test(df_sbsa_wide_profdiff$neuro_sqdiff_t2 - df_sbsa_wide_profdiff$neuro_sqdiff_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$neuro_sqdiff_t2 - df_sbsa_wide_profdiff$neuro_sqdiff_t1
W = 0.91151, p-value < 2.2e-16lillie.test(df_sbsa_wide_profdiff$neuro_sqdiff_t2 - df_sbsa_wide_profdiff$neuro_sqdiff_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$neuro_sqdiff_t2 - df_sbsa_wide_profdiff$neuro_sqdiff_t1
D = 0.11685, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa_wide_profdiff$neuro_sqdiff_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 609 3.38 3.48 2.25 2.8 2.68 0 16 16 1.4 1.42 0.14psych::describe(df_sbsa_wide_profdiff$neuro_sqdiff_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 615 2.69 3.1 1.56 2.08 1.95 0 15.34 15.34 1.75 2.83 0.13# openn
shapiro.test(df_sbsa_wide_profdiff$openn_sqdiff_t2 - df_sbsa_wide_profdiff$openn_sqdiff_t1)
Shapiro-Wilk normality test
data: df_sbsa_wide_profdiff$openn_sqdiff_t2 - df_sbsa_wide_profdiff$openn_sqdiff_t1
W = 0.7943, p-value < 2.2e-16lillie.test(df_sbsa_wide_profdiff$openn_sqdiff_t2 - df_sbsa_wide_profdiff$openn_sqdiff_t1)
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa_wide_profdiff$openn_sqdiff_t2 - df_sbsa_wide_profdiff$openn_sqdiff_t1
D = 0.19404, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa_wide_profdiff$openn_sqdiff_t1) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 609 0.46 0.76 0.11 0.28 0.16 0 5.06 5.06 2.9 10 0.03psych::describe(df_sbsa_wide_profdiff$openn_sqdiff_t2) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 615 0.37 0.61 0.11 0.23 0.15 0 4.34 4.34 3.07 12.1 0.02Results summary:
mod_traits_sqdiff <- df_sbsa_wide_profdiff %>%
select(-starts_with("profile")) %>%
pivot_longer(-c(pid),
names_to = c("test", "time"), values_to = "score",
names_pattern = "(.*)_(t1|t2)") %>%
filter(!is.na(score)) %>%
group_by(pid, test) %>%
mutate(assessments = n()) %>%
ungroup() %>%
filter(assessments==2) %>%
select(-assessments) %>%
group_nest(test) %>%
mutate(t_tests = map(data, ~t.test(score ~ time, data = .x, paired = TRUE))) %>%
pull(t_tests) %>%
purrr::set_names(names(b5_vars))
mod_traits_unlist <- mod_traits_sqdiff[[1]] %>% tidy()
for (i in 2:length(mod_traits_sqdiff)) {
mod_traits_unlist <- bind_rows(mod_traits_unlist, mod_traits_sqdiff[[i]] %>% tidy())
}
kable(mod_traits_unlist %>% mutate(outcome = names(b5_vars)) %>% select(outcome, everything()), digits = 3)| outcome | estimate | statistic | p.value | parameter | conf.low | conf.high | method | alternative |
|---|---|---|---|---|---|---|---|---|
| extraversion | 0.090 | 2.318 | 0.021 | 604 | 0.014 | 0.166 | Paired t-test | two.sided |
| agreeableness | 0.162 | 5.340 | 0.000 | 604 | 0.103 | 0.222 | Paired t-test | two.sided |
| conscientiousness | 0.641 | 4.461 | 0.000 | 604 | 0.359 | 0.924 | Paired t-test | two.sided |
| neuroticism | 0.444 | 5.059 | 0.000 | 604 | 0.272 | 0.617 | Paired t-test | two.sided |
| openness | 0.154 | 4.210 | 0.000 | 604 | 0.082 | 0.226 | Paired t-test | two.sided |
| sociability | 0.330 | 5.001 | 0.000 | 604 | 0.200 | 0.459 | Paired t-test | two.sided |
| assertiveness | 0.150 | 3.175 | 0.002 | 604 | 0.057 | 0.243 | Paired t-test | two.sided |
| energy | 0.939 | 7.455 | 0.000 | 604 | 0.691 | 1.186 | Paired t-test | two.sided |
| compassion | 0.485 | 4.997 | 0.000 | 604 | 0.295 | 0.676 | Paired t-test | two.sided |
| respectfulness | 0.492 | 7.398 | 0.000 | 604 | 0.361 | 0.622 | Paired t-test | two.sided |
| trust | 0.221 | 3.159 | 0.002 | 604 | 0.084 | 0.358 | Paired t-test | two.sided |
| organization | 0.683 | 7.025 | 0.000 | 604 | 0.492 | 0.875 | Paired t-test | two.sided |
| productiveness | 0.097 | 3.474 | 0.001 | 604 | 0.042 | 0.151 | Paired t-test | two.sided |
| responsibility | 0.391 | 4.111 | 0.000 | 604 | 0.204 | 0.578 | Paired t-test | two.sided |
| anxiety | 0.538 | 5.193 | 0.000 | 604 | 0.334 | 0.741 | Paired t-test | two.sided |
| depression | 0.156 | 3.703 | 0.000 | 604 | 0.073 | 0.239 | Paired t-test | two.sided |
| volatility | 0.247 | 3.703 | 0.000 | 604 | 0.116 | 0.378 | Paired t-test | two.sided |
| curiosity | 0.787 | 7.704 | 0.000 | 604 | 0.586 | 0.988 | Paired t-test | two.sided |
| aesthetic | 0.351 | 4.895 | 0.000 | 604 | 0.210 | 0.492 | Paired t-test | two.sided |
| imagination | 0.685 | 6.148 | 0.000 | 604 | 0.466 | 0.904 | Paired t-test | two.sided |
# effect sizes
cohend_cust = function(outcome, df) {
form = as.formula(paste0(outcome, " ~ time"))
psych::cohen.d(form, data = df)
}
d_trait <- cohend_cust(paste0(str_trunc(names(b5_vars)[1], 5, ellipsis = ""), "_sqdiff"), df_sbsa)
d_traits_unlist <- as_tibble(d_trait$cohen.d)
for (i in 2:length(names(b5_vars))) {
d_trait <- cohend_cust(paste0(str_trunc(names(b5_vars)[i], 5, ellipsis = ""), "_sqdiff"), df_sbsa)
d_traits_unlist <- bind_rows(d_traits_unlist, as_tibble(d_trait$cohen.d))
}
kable(d_traits_unlist %>% mutate(outcome = names(b5_vars)) %>% select(outcome, everything()), digits = 3)| outcome | lower | effect | upper |
|---|---|---|---|
| extraversion | -0.383 | -0.271 | -0.158 |
| agreeableness | -0.307 | -0.195 | -0.082 |
| conscientiousness | -0.296 | -0.183 | -0.071 |
| neuroticism | -0.323 | -0.211 | -0.099 |
| openness | -0.244 | -0.132 | -0.019 |
| sociability | -0.378 | -0.265 | -0.153 |
| assertiveness | -0.320 | -0.208 | -0.096 |
| energy | -0.320 | -0.208 | -0.095 |
| compassion | -0.289 | -0.177 | -0.065 |
| respectfulness | -0.263 | -0.151 | -0.039 |
| trust | -0.320 | -0.208 | -0.095 |
| organization | -0.281 | -0.169 | -0.057 |
| productiveness | -0.297 | -0.184 | -0.072 |
| responsibility | -0.257 | -0.145 | -0.033 |
| anxiety | -0.269 | -0.157 | -0.044 |
| depression | -0.357 | -0.244 | -0.132 |
| volatility | -0.315 | -0.202 | -0.090 |
| curiosity | -0.255 | -0.143 | -0.031 |
| aesthetic | -0.224 | -0.112 | 0.000 |
| imagination | -0.215 | -0.103 | 0.009 |
Plot (traits/facets sorted by average size of squared differences)
sqdiff_df_plot <- df_sbsa %>%
select(pid, time, all_of(paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_sqdiff"))) %>%
pivot_longer(-c(pid, time), names_to = "trait", values_to = "sqdiff") %>%
group_by(trait) %>%
mutate(cur_group_id()) %>%
ungroup() %>%
mutate(trait2 = fct_recode(trait, !!! setNames(paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_sqdiff"),
str_to_title(names(b5_vars)))),
trait2 = fct_reorder(trait2, sqdiff, .desc = T))
plot_sqdiff <- ggplot(sqdiff_df_plot) +
aes(x = as.factor(time), y = sqdiff) +
geom_boxplot() +
geom_violin(fill = NA) +
ggforce::facet_wrap_paginate(~trait2, ncol=3, nrow=2) +
labs(x = "Measurement Occasion", y = "Squared difference") +
theme_bw()
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 1)
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 2)
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 3)
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 4)
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 5)
In the skill-building group, there will be a correlation between change goals and change in current-self ratings but not change in ideal-self ratings.
We will test this one domain/facet at a time. We will use both general continuous change goal score as well as trait-specific change goals. To test this hypothesis, we will estimate the mean-level differences across time for both current and ideal trait ratings using latent change models and correlate change goals with the change variable from those models.
Reshape and split data set by intervention group:
df_sbsa_wide_pers_sb <- df_sbsa %>%
filter(rando=="Skill-Building") %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sb07")), # facet-specific change goals
sb06_01) %>% # general change goal
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with("sb07"), sb06_01)) %>%
select(-c(sb07_01_t2, sb07_02_t2, sb07_03_t2, sb07_04_t2, sb07_05_t2,
sb07_06_t2, sb07_07_t2, sb07_08_t2, sb07_09_t2, sb07_10_t2,
sb07_11_t2, sb07_12_t2, sb07_13_t2, sb07_14_t2, sb07_15_t2, sb06_01_t2))
# colnames(df_sbsa_wide_pers_sb)
group_assign <- df_sbsa %>% select(pid, rando) %>% unique()
df_sbsa_wide_pers_sb <- df_sbsa_wide_pers %>% left_join(group_assign) %>% filter(rando=="Skill-Building") %>% select(-rando) %>%
left_join(df_sbsa_wide_pers_sb)Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_curr_hyp4 <- '
extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
extra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1
extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1
extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1
extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2
extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2
extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2
extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_extra_curr_hyp4 <- lavaan(mi_lcs_extra_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 18 | 4143.568 | 4211.229 | 0.982 | 0.063 | 0.082 |
# parameters of interest
params_lcs_extra_curr_hyp4 <- broom::tidy(fit_mi_lcs_extra_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.555 | NA | NA |
| d_extra_1 ~1 | 0.523 | 0.280 | 0.766 | 1.594 | 4.219 | 0.000 |
| extra_t1 ~1 | 2.755 | 2.677 | 2.832 | 4.757 | 69.576 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.101 | 0.063 | 0.139 | 0.939 | 5.225 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.139 | -0.224 | -0.054 | -0.246 | -3.210 | 0.001 |
| d_extra_1 ~~ sb06_01_t1 | 0.052 | 0.001 | 0.102 | 0.163 | 2.006 | 0.045 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.224 | 0.000 |
| sb06_01_t1 ~1 | 3.078 | 2.965 | 3.190 | 3.076 | 53.689 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_ideal_hyp4 <- '
extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
extra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_extra_ideal_hyp4 <- lavaan(mi_lcs_extra_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 18 | 3751.37 | 3819.03 | 0.997 | 0.021 | 0.045 |
# parameters of interest
params_lcs_extra_ideal_hyp4 <- broom::tidy(fit_mi_lcs_extra_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.941 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.631 | NA | NA |
| d_extra_1 ~1 | 0.588 | 0.054 | 1.123 | 2.205 | 2.159 | 0.031 |
| extra_t1 ~1 | 3.855 | 3.791 | 3.920 | 9.688 | 117.306 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.067 | 0.037 | 0.097 | 0.943 | 4.356 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.160 | -0.297 | -0.023 | -0.239 | -2.283 | 0.022 |
| d_extra_1 ~~ sb06_01_t1 | 0.035 | -0.006 | 0.076 | 0.135 | 1.673 | 0.094 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.852 | 1.149 | 1.000 | 13.226 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.963 | 3.188 | 3.074 | 53.710 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_curr_specif_hyp4 <- '
extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
goals =~ 1*sb07_01_t1 + sb07_02_t1 + sb07_03_t1 # latent change goal variable (three facets per trait)
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
extra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1
extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1
extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1
extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2
extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2
extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2
extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_01_t1 ~~ sb07_01_t1
sb07_02_t1 ~~ sb07_02_t1
sb07_03_t1 ~~ sb07_03_t1
sb07_01_t1 ~ 1
sb07_02_t1 ~ 1
sb07_03_t1 ~ 1
'
fit_mi_lcs_extra_curr_specif_hyp4 <- lavaan(mi_lcs_extra_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 25 | 6179.958 | 6273.93 | 0.941 | 0.091 | 0.131 |
# parameters of interest
params_lcs_extra_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_extra_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"goals ~~ d_extra_1", "goals ~~ goals", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.989 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.564 | NA | NA |
| d_extra_1 ~1 | 0.555 | 0.315 | 0.796 | 1.683 | 4.531 | 0.000 |
| extra_t1 ~1 | 2.755 | 2.678 | 2.833 | 4.761 | 69.636 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.101 | 0.064 | 0.139 | 0.930 | 5.274 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.151 | -0.236 | -0.067 | -0.265 | -3.504 | 0.000 |
| goals ~~ d_extra_1 | 0.008 | -0.032 | 0.048 | 0.044 | 0.374 | 0.708 |
| goals ~~ goals | 0.293 | 0.054 | 0.531 | 1.000 | 2.407 | 0.016 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_ideal_specif_hyp4 <- '
extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
goals =~ 1*sb07_01_t1 + sb07_02_t1 + sb07_03_t1 # latent change goal variable (three facets per trait)
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
extra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_01_t1 ~~ sb07_01_t1
sb07_02_t1 ~~ sb07_02_t1
sb07_03_t1 ~~ sb07_03_t1
sb07_01_t1 ~ 1
sb07_02_t1 ~ 1
sb07_03_t1 ~ 1
'
fit_mi_lcs_extra_ideal_specif_hyp4 <- lavaan(mi_lcs_extra_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 25 | 5785.581 | 5879.554 | 0.963 | 0.058 | 0.088 |
# parameters of interest
params_lcs_extra_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_extra_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"goals ~~ d_extra_1", "goals ~~ goals", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.940 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.630 | NA | NA |
| d_extra_1 ~1 | 0.584 | 0.026 | 1.141 | 2.188 | 2.051 | 0.040 |
| extra_t1 ~1 | 3.855 | 3.791 | 3.920 | 9.679 | 117.312 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.067 | 0.037 | 0.098 | 0.944 | 4.314 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.159 | -0.303 | -0.015 | -0.237 | -2.165 | 0.030 |
| goals ~~ d_extra_1 | 0.014 | -0.029 | 0.057 | 0.096 | 0.630 | 0.528 |
| goals ~~ goals | 0.312 | 0.041 | 0.583 | 1.000 | 2.259 | 0.024 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_curr_hyp4 <- '
agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
agree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1
agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1
agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1
agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2
agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2
agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2
agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_agree_curr_hyp4 <- lavaan(mi_lcs_agree_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 18 | 3724.455 | 3792.115 | 0.991 | 0.037 | 0.034 |
# parameters of interest
params_lcs_agree_curr_hyp4 <- broom::tidy(fit_mi_lcs_agree_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.057 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.543 | NA | NA |
| d_agree_1 ~1 | 0.693 | 0.400 | 0.986 | 3.178 | 4.639 | 0.000 |
| agree_t1 ~1 | 3.344 | 3.278 | 3.410 | 7.881 | 99.472 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.041 | 0.020 | 0.063 | 0.871 | 3.716 | 0.000 |
| d_agree_1 ~ agree_t1 | -0.185 | -0.270 | -0.100 | -0.360 | -4.246 | 0.000 |
| d_agree_1 ~~ sb06_01_t1 | -0.018 | -0.052 | 0.017 | -0.087 | -1.006 | 0.314 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.852 | 1.149 | 1.000 | 13.224 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.187 | 3.074 | 53.631 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_ideal_hyp4 <- '
agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
agree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_agree_ideal_hyp4 <- lavaan(mi_lcs_agree_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 18 | 3843.95 | 3911.61 | 0.995 | 0.028 | 0.035 |
# parameters of interest
params_lcs_agree_ideal_hyp4 <- broom::tidy(fit_mi_lcs_agree_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.011 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.632 | NA | NA |
| d_agree_1 ~1 | 0.847 | 0.272 | 1.423 | 3.184 | 2.885 | 0.004 |
| agree_t1 ~1 | 3.941 | 3.870 | 4.013 | 9.267 | 108.592 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.063 | 0.030 | 0.096 | 0.891 | 3.754 | 0.000 |
| d_agree_1 ~ agree_t1 | -0.206 | -0.348 | -0.065 | -0.330 | -2.857 | 0.004 |
| d_agree_1 ~~ sb06_01_t1 | -0.002 | -0.046 | 0.042 | -0.009 | -0.098 | 0.922 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.074 | 53.671 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_curr_specif_hyp4 <- '
agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
goals =~ 1*sb07_04_t1 + sb07_05_t1 + sb07_06_t1 # latent change goal variable (three facets per trait)
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
agree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1
agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1
agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1
agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2
agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2
agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2
agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_04_t1 ~~ sb07_04_t1
sb07_05_t1 ~~ sb07_05_t1
sb07_06_t1 ~~ sb07_06_t1
sb07_04_t1 ~ 1
sb07_05_t1 ~ 1
sb07_06_t1 ~ 1
'
fit_mi_lcs_agree_curr_specif_hyp4 <- lavaan(mi_lcs_agree_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 25 | 5864.338 | 5958.31 | 0.961 | 0.065 | 0.085 |
# parameters of interest
params_lcs_agree_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_agree_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"goals ~~ d_agree_1", "goals ~~ goals", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.056 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.541 | NA | NA |
| d_agree_1 ~1 | 0.686 | 0.396 | 0.977 | 3.151 | 4.627 | 0.000 |
| agree_t1 ~1 | 3.344 | 3.278 | 3.410 | 7.870 | 99.446 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.041 | 0.020 | 0.063 | 0.873 | 3.732 | 0.000 |
| d_agree_1 ~ agree_t1 | -0.183 | -0.268 | -0.097 | -0.357 | -4.181 | 0.000 |
| goals ~~ d_agree_1 | 0.001 | -0.039 | 0.041 | 0.006 | 0.056 | 0.955 |
| goals ~~ goals | 0.900 | 0.565 | 1.234 | 1.000 | 5.275 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_ideal_specif_hyp4 <- '
agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
goals =~ 1*sb07_04_t1 + sb07_05_t1 + sb07_06_t1 # latent change goal variable (three facets per trait)
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
agree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_04_t1 ~~ sb07_04_t1
sb07_05_t1 ~~ sb07_05_t1
sb07_06_t1 ~~ sb07_06_t1
sb07_04_t1 ~ 1
sb07_05_t1 ~ 1
sb07_06_t1 ~ 1
'
fit_mi_lcs_agree_ideal_specif_hyp4 <- lavaan(mi_lcs_agree_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 25 | 5980.062 | 6074.034 | 0.991 | 0.031 | 0.034 |
# parameters of interest
params_lcs_agree_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_agree_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"goals ~~ d_agree_1", "goals ~~ goals", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.012 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.632 | NA | NA |
| d_agree_1 ~1 | 0.849 | 0.288 | 1.410 | 3.199 | 2.965 | 0.003 |
| agree_t1 ~1 | 3.942 | 3.870 | 4.013 | 9.274 | 108.628 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.063 | 0.030 | 0.095 | 0.890 | 3.756 | 0.000 |
| d_agree_1 ~ agree_t1 | -0.207 | -0.345 | -0.069 | -0.331 | -2.940 | 0.003 |
| goals ~~ d_agree_1 | -0.035 | -0.083 | 0.012 | -0.148 | -1.459 | 0.145 |
| goals ~~ goals | 0.897 | 0.555 | 1.238 | 1.000 | 5.150 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_curr_hyp4 <- '
consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
consc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1
consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1
consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1
consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2
consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2
consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2
consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_consc_curr_hyp4 <- lavaan(mi_lcs_consc_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 18 | 3900.313 | 3967.973 | 0.994 | 0.043 | 0.062 |
# parameters of interest
params_lcs_consc_curr_hyp4 <- broom::tidy(fit_mi_lcs_consc_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.038 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.451 | NA | NA |
| d_consc_1 ~1 | 0.551 | 0.328 | 0.774 | 1.714 | 4.839 | 0.000 |
| consc_t1 ~1 | 3.271 | 3.178 | 3.363 | 4.420 | 69.313 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.094 | 0.059 | 0.129 | 0.910 | 5.293 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.130 | -0.193 | -0.067 | -0.300 | -4.053 | 0.000 |
| d_consc_1 ~~ sb06_01_t1 | 0.011 | -0.034 | 0.057 | 0.037 | 0.484 | 0.628 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.074 | 53.685 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_ideal_hyp4 <- '
consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
consc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_consc_ideal_hyp4 <- lavaan(mi_lcs_consc_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 18 | 3274.277 | 3341.937 | 0.996 | 0.024 | 0.029 |
# parameters of interest
params_lcs_consc_ideal_hyp4 <- broom::tidy(fit_mi_lcs_consc_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.028 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.701 | NA | NA |
| d_consc_1 ~1 | 1.140 | 0.508 | 1.773 | 3.913 | 3.534 | 0.000 |
| consc_t1 ~1 | 4.376 | 4.319 | 4.432 | 10.236 | 151.704 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.073 | 0.039 | 0.106 | 0.856 | 4.278 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.259 | -0.400 | -0.118 | -0.380 | -3.599 | 0.000 |
| d_consc_1 ~~ sb06_01_t1 | 0.011 | -0.036 | 0.058 | 0.040 | 0.447 | 0.655 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.187 | 3.074 | 53.715 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_curr_specif_hyp4 <- '
consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
goals =~ 1*sb07_07_t1 + sb07_08_t1 + sb07_09_t1 # latent change goal variable (three facets per trait)
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
consc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1
consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1
consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1
consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2
consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2
consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2
consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_07_t1 ~~ sb07_07_t1
sb07_08_t1 ~~ sb07_08_t1
sb07_09_t1 ~~ sb07_09_t1
sb07_07_t1 ~ 1
sb07_08_t1 ~ 1
sb07_09_t1 ~ 1
'
fit_mi_lcs_consc_curr_specif_hyp4 <- lavaan(mi_lcs_consc_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 25 | 5982.01 | 6075.982 | 0.94 | 0.114 | 0.213 |
# parameters of interest
params_lcs_consc_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_consc_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"goals ~~ d_consc_1", "goals ~~ goals", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.042 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.455 | NA | NA |
| d_consc_1 ~1 | 0.565 | 0.300 | 0.831 | 1.752 | 4.181 | 0.000 |
| consc_t1 ~1 | 3.271 | 3.178 | 3.363 | 4.421 | 69.316 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.094 | 0.059 | 0.129 | 0.905 | 5.278 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.135 | -0.211 | -0.058 | -0.309 | -3.448 | 0.001 |
| goals ~~ d_consc_1 | -0.003 | -0.068 | 0.062 | -0.009 | -0.093 | 0.926 |
| goals ~~ goals | 1.296 | 0.981 | 1.611 | 1.000 | 8.063 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_ideal_specif_hyp4 <- '
consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
goals =~ 1*sb07_07_t1 + sb07_08_t1 + sb07_09_t1 # latent change goal variable (three facets per trait)
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
consc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_07_t1 ~~ sb07_07_t1
sb07_08_t1 ~~ sb07_08_t1
sb07_09_t1 ~~ sb07_09_t1
sb07_07_t1 ~ 1
sb07_08_t1 ~ 1
sb07_09_t1 ~ 1
'
fit_mi_lcs_consc_ideal_specif_hyp4 <- lavaan(mi_lcs_consc_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 25 | 5355.658 | 5449.631 | 1 | 0 | 0.028 |
# parameters of interest
params_lcs_consc_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_consc_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"goals ~~ d_consc_1", "goals ~~ goals", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.026 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.699 | NA | NA |
| d_consc_1 ~1 | 1.132 | 0.502 | 1.762 | 3.886 | 3.521 | 0.000 |
| consc_t1 ~1 | 4.376 | 4.319 | 4.432 | 10.225 | 151.673 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.073 | 0.039 | 0.106 | 0.857 | 4.280 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.257 | -0.398 | -0.116 | -0.378 | -3.584 | 0.000 |
| goals ~~ d_consc_1 | -0.016 | -0.067 | 0.035 | -0.051 | -0.603 | 0.546 |
| goals ~~ goals | 1.297 | 0.980 | 1.614 | 1.000 | 8.026 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_curr_hyp4 <- '
neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
neuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_neuro_curr_hyp4 <- lavaan(mi_lcs_neuro_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 18 | 4489.18 | 4556.84 | 0.971 | 0.09 | 0.102 |
# parameters of interest
params_lcs_neuro_curr_hyp4 <- broom::tidy(fit_mi_lcs_neuro_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.003 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.590 | NA | NA |
| d_neuro_1 ~1 | 0.385 | 0.117 | 0.652 | 0.779 | 2.819 | 0.005 |
| neuro_t1 ~1 | 3.532 | 3.432 | 3.631 | 4.208 | 69.620 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.222 | 0.151 | 0.293 | 0.911 | 6.147 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.176 | -0.251 | -0.100 | -0.298 | -4.535 | 0.000 |
| d_neuro_1 ~~ sb06_01_t1 | -0.045 | -0.109 | 0.018 | -0.096 | -1.407 | 0.159 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.852 | 1.149 | 1.000 | 13.224 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.964 | 3.188 | 3.075 | 53.760 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_ideal_hyp4 <- '
neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_neuro_ideal_hyp4 <- lavaan(mi_lcs_neuro_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 18 | 3517.147 | 3584.807 | 1 | 0.009 | 0.042 |
# parameters of interest
params_lcs_neuro_ideal_hyp4 <- broom::tidy(fit_mi_lcs_neuro_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.983 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.706 | NA | NA |
| d_neuro_1 ~1 | 0.368 | 0.142 | 0.594 | 1.064 | 3.190 | 0.001 |
| neuro_t1 ~1 | 1.643 | 1.581 | 1.705 | 3.408 | 51.983 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.106 | 0.066 | 0.146 | 0.888 | 5.193 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.240 | -0.386 | -0.095 | -0.335 | -3.247 | 0.001 |
| d_neuro_1 ~~ sb06_01_t1 | -0.027 | -0.080 | 0.025 | -0.084 | -1.033 | 0.302 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.218 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.963 | 3.188 | 3.074 | 53.690 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_curr_specif_hyp4 <- '
neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
goals =~ 1*sb07_10_t1 + sb07_11_t1 + sb07_12_t1 # latent change goal variable (three facets per trait)
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
neuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_10_t1 ~~ sb07_10_t1
sb07_11_t1 ~~ sb07_11_t1
sb07_12_t1 ~~ sb07_12_t1
sb07_10_t1 ~ 1
sb07_11_t1 ~ 1
sb07_12_t1 ~ 1
'
fit_mi_lcs_neuro_curr_specif_hyp4 <- lavaan(mi_lcs_neuro_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 25 | 6670.967 | 6764.94 | 0.906 | 0.14 | 0.22 |
# parameters of interest
params_lcs_neuro_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_neuro_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"goals ~~ d_neuro_1", "goals ~~ goals", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.073 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.676 | NA | NA |
| d_neuro_1 ~1 | 0.698 | 0.347 | 1.050 | 1.320 | 3.897 | 0.000 |
| neuro_t1 ~1 | 3.532 | 3.433 | 3.631 | 4.207 | 69.640 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.230 | 0.155 | 0.306 | 0.824 | 6.018 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.265 | -0.365 | -0.164 | -0.420 | -5.169 | 0.000 |
| goals ~~ d_neuro_1 | 0.121 | 0.014 | 0.229 | 0.229 | 2.218 | 0.027 |
| goals ~~ goals | 1.216 | 0.853 | 1.579 | 1.000 | 6.566 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_ideal_specif_hyp4 <- '
neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
goals =~ 1*sb07_10_t1 + sb07_11_t1 + sb07_12_t1 # latent change goal variable (three facets per trait)
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_10_t1 ~~ sb07_10_t1
sb07_11_t1 ~~ sb07_11_t1
sb07_12_t1 ~~ sb07_12_t1
sb07_10_t1 ~ 1
sb07_11_t1 ~ 1
sb07_12_t1 ~ 1
'
fit_mi_lcs_neuro_ideal_specif_hyp4 <- lavaan(mi_lcs_neuro_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 25 | 5704.852 | 5798.825 | 0.992 | 0.032 | 0.031 |
# parameters of interest
params_lcs_neuro_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_neuro_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"goals ~~ d_neuro_1", "goals ~~ goals", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.979 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.700 | NA | NA |
| d_neuro_1 ~1 | 0.357 | 0.133 | 0.581 | 1.036 | 3.129 | 0.002 |
| neuro_t1 ~1 | 1.643 | 1.581 | 1.705 | 3.407 | 51.981 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.106 | 0.066 | 0.146 | 0.893 | 5.160 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.234 | -0.377 | -0.090 | -0.327 | -3.194 | 0.001 |
| goals ~~ d_neuro_1 | 0.001 | -0.055 | 0.057 | 0.003 | 0.043 | 0.966 |
| goals ~~ goals | 1.248 | 0.887 | 1.609 | 1.000 | 6.773 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_curr_hyp4 <- '
openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
openn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1
openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1
openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1
openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2
openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2
openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2
openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_openn_curr_hyp4 <- lavaan(mi_lcs_openn_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 18 | 4017.956 | 4085.617 | 0.996 | 0.028 | 0.024 |
# parameters of interest
params_lcs_openn_curr_hyp4 <- broom::tidy(fit_mi_lcs_openn_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.913 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.393 | NA | NA |
| d_openn_1 ~1 | 0.036 | -0.270 | 0.342 | 0.191 | 0.232 | 0.817 |
| openn_t1 ~1 | 3.553 | 3.486 | 3.619 | 8.064 | 104.804 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.036 | 0.014 | 0.058 | 1.000 | 3.248 | 0.001 |
| d_openn_1 ~ openn_t1 | 0.008 | -0.078 | 0.093 | 0.018 | 0.178 | 0.858 |
| d_openn_1 ~~ sb06_01_t1 | -0.012 | -0.050 | 0.027 | -0.061 | -0.594 | 0.552 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.220 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.187 | 3.074 | 53.620 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_ideal_hyp4 <- '
openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
openn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_openn_ideal_hyp4 <- lavaan(mi_lcs_openn_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 18 | 3776.641 | 3844.302 | 0.982 | 0.05 | 0.058 |
# parameters of interest
params_lcs_openn_ideal_hyp4 <- broom::tidy(fit_mi_lcs_openn_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.977 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.631 | NA | NA |
| d_openn_1 ~1 | 0.713 | 0.115 | 1.312 | 2.850 | 2.335 | 0.020 |
| openn_t1 ~1 | 3.874 | 3.810 | 3.938 | 10.000 | 118.343 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.057 | 0.027 | 0.088 | 0.918 | 3.725 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.185 | -0.335 | -0.034 | -0.286 | -2.408 | 0.016 |
| d_openn_1 ~~ sb06_01_t1 | -0.028 | -0.075 | 0.019 | -0.115 | -1.154 | 0.249 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.150 | 1.000 | 13.234 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.187 | 3.073 | 53.653 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_curr_specif_hyp4 <- '
openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
goals =~ 1*sb07_13_t1 + sb07_14_t1 + sb07_15_t1 # latent change goal variable (three facets per trait)
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
openn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1
openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1
openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1
openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2
openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2
openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2
openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_13_t1 ~~ sb07_13_t1
sb07_14_t1 ~~ sb07_14_t1
sb07_15_t1 ~~ sb07_15_t1
sb07_13_t1 ~ 1
sb07_14_t1 ~ 1
sb07_15_t1 ~ 1
'
fit_mi_lcs_openn_curr_specif_hyp4 <- lavaan(mi_lcs_openn_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 25 | 5973.64 | 6067.613 | 0.97 | 0.066 | 0.1 |
# parameters of interest
params_lcs_openn_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_openn_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"goals ~~ d_openn_1", "goals ~~ goals", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.897 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.382 | NA | NA |
| d_openn_1 ~1 | -0.046 | -0.360 | 0.268 | -0.247 | -0.289 | 0.773 |
| openn_t1 ~1 | 3.553 | 3.487 | 3.619 | 8.102 | 104.865 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.035 | 0.013 | 0.056 | 0.995 | 3.166 | 0.002 |
| d_openn_1 ~ openn_t1 | 0.031 | -0.057 | 0.119 | 0.072 | 0.689 | 0.491 |
| goals ~~ d_openn_1 | 0.029 | 0.003 | 0.054 | 0.247 | 2.221 | 0.026 |
| goals ~~ goals | 0.395 | 0.222 | 0.568 | 1.000 | 4.478 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_ideal_specif_hyp4 <- '
openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
goals =~ 1*sb07_13_t1 + sb07_14_t1 + sb07_15_t1 # latent change goal variable (three facets per trait)
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
openn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_13_t1 ~~ sb07_13_t1
sb07_14_t1 ~~ sb07_14_t1
sb07_15_t1 ~~ sb07_15_t1
sb07_13_t1 ~ 1
sb07_14_t1 ~ 1
sb07_15_t1 ~ 1
'
fit_mi_lcs_openn_ideal_specif_hyp4 <- lavaan(mi_lcs_openn_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 25 | 5737.696 | 5831.668 | 0.997 | 0.02 | 0.038 |
# parameters of interest
params_lcs_openn_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_openn_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"goals ~~ d_openn_1", "goals ~~ goals", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.987 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.645 | NA | NA |
| d_openn_1 ~1 | 0.773 | 0.208 | 1.339 | 3.057 | 2.683 | 0.007 |
| openn_t1 ~1 | 3.874 | 3.810 | 3.938 | 9.998 | 118.367 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.058 | 0.028 | 0.088 | 0.906 | 3.747 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.200 | -0.342 | -0.058 | -0.307 | -2.767 | 0.006 |
| goals ~~ d_openn_1 | 0.015 | -0.014 | 0.043 | 0.097 | 0.993 | 0.321 |
| goals ~~ goals | 0.395 | 0.223 | 0.568 | 1.000 | 4.501 | 0.000 |
Run models for all facets with a template & loop:
# create template:
facet_template <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
d_facet_1 ~ facet_t1 # This estimates the self-feedback parameter
d_facet_1 ~~ ind_goal # estimates the covariance/correlation with change goal variable
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# loop across 2 BFI versions (combined pre&post current/ideal)
for (j in 5:length(bfi_versions)) {
items = paste0(bfi_versions[[j]], item_nrs)
# loop across 2 different goal operationalizations (sb06_01_t1 & sb07_XX_t1)
for (k in 1:2) {
if (k==1) {
goal_op = "sb06_01_t1"
} else{
goal_op = paste0("sb07_", str_pad(i-5, 2, pad = "0"), "_t1")
}
template_filled <- str_replace_all(facet_template,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = goal_op))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
# save to environment
if (k==1) {
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_hyp4")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_hyp4")), facet_model_fit))
} else{
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_specif_hyp4")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_specif_hyp4")), facet_model_fit))
}
}
}
} Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7666.153 | 7748.849 | 0.944 | 0.081 | 0.067 |
# parameters of interest
params_lcs_socia_curr_hyp4 <- broom::tidy(fit_mi_lcs_socia_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.001 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.618 | NA | NA |
| d_socia_1 ~1 | 0.745 | 0.455 | 1.035 | 1.414 | 5.032 | 0.000 |
| socia_t1 ~1 | 2.780 | 2.647 | 2.913 | 3.256 | 40.920 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.251 | 0.150 | 0.352 | 0.904 | 4.861 | 0.000 |
| d_socia_1 ~ socia_t1 | -0.192 | -0.289 | -0.094 | -0.311 | -3.839 | 0.000 |
| d_socia_1 ~~ sb06_01_t1 | 0.081 | 0.001 | 0.161 | 0.162 | 1.983 | 0.047 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.221 | 0.000 |
| sb06_01_t1 ~1 | 3.077 | 2.965 | 3.190 | 3.076 | 53.648 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7487.096 | 7569.792 | 0.968 | 0.044 | 0.051 |
# parameters of interest
params_lcs_socia_ideal_hyp4 <- broom::tidy(fit_mi_lcs_socia_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.957 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.500 | NA | NA |
| d_socia_1 ~1 | 0.355 | -0.296 | 1.006 | 1.559 | 1.069 | 0.285 |
| socia_t1 ~1 | 4.185 | 4.104 | 4.266 | 9.596 | 101.705 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.050 | -0.001 | 0.101 | 0.970 | 1.939 | 0.052 |
| d_socia_1 ~ socia_t1 | -0.091 | -0.246 | 0.065 | -0.173 | -1.144 | 0.253 |
| d_socia_1 ~~ sb06_01_t1 | 0.042 | -0.011 | 0.096 | 0.187 | 1.540 | 0.124 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.852 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.187 | 3.074 | 53.706 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7779.632 | 7862.328 | 0.911 | 0.104 | 0.12 |
# parameters of interest
params_lcs_socia_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_socia_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sb07_01_t1", "sb07_01_t1 ~~ sb07_01_t1", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.032 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.656 | NA | NA |
| d_socia_1 ~1 | 0.856 | 0.541 | 1.171 | 1.588 | 5.329 | 0.000 |
| socia_t1 ~1 | 2.781 | 2.648 | 2.914 | 3.278 | 41.012 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.252 | 0.151 | 0.353 | 0.866 | 4.885 | 0.000 |
| d_socia_1 ~ socia_t1 | -0.232 | -0.338 | -0.127 | -0.366 | -4.317 | 0.000 |
| d_socia_1 ~~ sb07_01_t1 | -0.058 | -0.154 | 0.037 | -0.097 | -1.197 | 0.231 |
| sb07_01_t1 ~~ sb07_01_t1 | 1.437 | 1.236 | 1.639 | 1.000 | 13.991 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7596.094 | 7678.789 | 0.941 | 0.061 | 0.07 |
# parameters of interest
params_lcs_socia_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_socia_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sb07_01_t1", "sb07_01_t1 ~~ sb07_01_t1", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.969 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.520 | NA | NA |
| d_socia_1 ~1 | 0.444 | -0.216 | 1.104 | 1.908 | 1.319 | 0.187 |
| socia_t1 ~1 | 4.185 | 4.104 | 4.265 | 9.650 | 101.713 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.052 | 0.002 | 0.102 | 0.957 | 2.019 | 0.044 |
| d_socia_1 ~ socia_t1 | -0.112 | -0.269 | 0.045 | -0.208 | -1.394 | 0.163 |
| d_socia_1 ~~ sb07_01_t1 | 0.062 | 0.000 | 0.124 | 0.228 | 1.959 | 0.050 |
| sb07_01_t1 ~~ sb07_01_t1 | 1.437 | 1.236 | 1.638 | 1.000 | 13.988 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 8008.14 | 8090.836 | 0.988 | 0.031 | 0.049 |
# parameters of interest
params_lcs_asser_curr_hyp4 <- broom::tidy(fit_mi_lcs_asser_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.997 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.351 | NA | NA |
| d_asser_1 ~1 | 0.231 | -0.106 | 0.569 | 1.155 | 1.344 | 0.179 |
| asser_t1 ~1 | 3.035 | 2.926 | 3.144 | 5.331 | 54.553 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.039 | -0.004 | 0.082 | 0.972 | 1.789 | 0.074 |
| d_asser_1 ~ asser_t1 | -0.059 | -0.171 | 0.053 | -0.168 | -1.036 | 0.300 |
| d_asser_1 ~~ sb06_01_t1 | 0.010 | -0.048 | 0.067 | 0.049 | 0.328 | 0.743 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.963 | 3.188 | 3.074 | 53.712 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7892.201 | 7974.897 | 0.996 | 0.012 | 0.041 |
# parameters of interest
params_lcs_asser_ideal_hyp4 <- broom::tidy(fit_mi_lcs_asser_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.933 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.584 | NA | NA |
| d_asser_1 ~1 | 0.456 | -0.276 | 1.189 | 2.419 | 1.222 | 0.222 |
| asser_t1 ~1 | 4.051 | 3.959 | 4.143 | 13.441 | 86.224 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.034 | 0.000 | 0.068 | 0.962 | 1.963 | 0.050 |
| d_asser_1 ~ asser_t1 | -0.122 | -0.300 | 0.057 | -0.194 | -1.338 | 0.181 |
| d_asser_1 ~~ sb06_01_t1 | 0.037 | -0.002 | 0.076 | 0.201 | 1.876 | 0.061 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.221 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.963 | 3.188 | 3.074 | 53.699 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 8105.365 | 8188.061 | 0.948 | 0.065 | 0.081 |
# parameters of interest
params_lcs_asser_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_asser_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sb07_02_t1", "sb07_02_t1 ~~ sb07_02_t1", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.994 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.347 | NA | NA |
| d_asser_1 ~1 | 0.219 | -0.127 | 0.565 | 1.100 | 1.240 | 0.215 |
| asser_t1 ~1 | 3.035 | 2.925 | 3.144 | 5.318 | 54.526 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.039 | -0.004 | 0.082 | 0.975 | 1.763 | 0.078 |
| d_asser_1 ~ asser_t1 | -0.055 | -0.171 | 0.061 | -0.158 | -0.931 | 0.352 |
| d_asser_1 ~~ sb07_02_t1 | 0.014 | -0.048 | 0.075 | 0.060 | 0.440 | 0.660 |
| sb07_02_t1 ~~ sb07_02_t1 | 1.377 | 1.195 | 1.559 | 1.000 | 14.838 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7993.148 | 8075.844 | 0.955 | 0.044 | 0.065 |
# parameters of interest
params_lcs_asser_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_asser_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sb07_02_t1", "sb07_02_t1 ~~ sb07_02_t1", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.934 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.585 | NA | NA |
| d_asser_1 ~1 | 0.458 | -0.292 | 1.209 | 2.410 | 1.197 | 0.231 |
| asser_t1 ~1 | 4.051 | 3.959 | 4.143 | 13.341 | 86.308 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.035 | 0.000 | 0.070 | 0.962 | 1.943 | 0.052 |
| d_asser_1 ~ asser_t1 | -0.122 | -0.305 | 0.061 | -0.195 | -1.311 | 0.190 |
| d_asser_1 ~~ sb07_02_t1 | 0.018 | -0.026 | 0.062 | 0.082 | 0.807 | 0.420 |
| sb07_02_t1 ~~ sb07_02_t1 | 1.377 | 1.195 | 1.559 | 1.000 | 14.837 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7950.008 | 8032.704 | 0.935 | 0.076 | 0.08 |
# parameters of interest
params_lcs_energ_curr_hyp4 <- broom::tidy(fit_mi_lcs_energ_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.550 | NA | NA |
| d_energ_1 ~1 | 0.265 | -0.031 | 0.560 | 1.069 | 1.756 | 0.079 |
| energ_t1 ~1 | 2.682 | 2.574 | 2.789 | 6.078 | 48.943 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.058 | 0.017 | 0.098 | 0.940 | 2.807 | 0.005 |
| d_energ_1 ~ energ_t1 | -0.137 | -0.247 | -0.028 | -0.245 | -2.460 | 0.014 |
| d_energ_1 ~~ sb06_01_t1 | -0.018 | -0.063 | 0.026 | -0.076 | -0.808 | 0.419 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.964 | 3.189 | 3.075 | 53.655 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7470.21 | 7552.905 | 0.946 | 0.048 | 0.051 |
# parameters of interest
params_lcs_energ_ideal_hyp4 <- broom::tidy(fit_mi_lcs_energ_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.936 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.659 | NA | NA |
| d_energ_1 ~1 | 0.425 | -0.067 | 0.918 | 1.581 | 1.691 | 0.091 |
| energ_t1 ~1 | 2.244 | 2.132 | 2.357 | 5.876 | 39.073 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.068 | 0.009 | 0.126 | 0.937 | 2.266 | 0.023 |
| d_energ_1 ~ energ_t1 | -0.177 | -0.405 | 0.051 | -0.252 | -1.525 | 0.127 |
| d_energ_1 ~~ sb06_01_t1 | -0.031 | -0.084 | 0.022 | -0.119 | -1.136 | 0.256 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.964 | 3.188 | 3.075 | 53.696 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 8080.601 | 8163.297 | 0.903 | 0.094 | 0.097 |
# parameters of interest
params_lcs_energ_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_energ_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sb07_03_t1", "sb07_03_t1 ~~ sb07_03_t1", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.006 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.581 | NA | NA |
| d_energ_1 ~1 | 0.360 | 0.053 | 0.666 | 1.414 | 2.302 | 0.021 |
| energ_t1 ~1 | 2.681 | 2.574 | 2.789 | 6.095 | 48.904 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.059 | 0.018 | 0.100 | 0.911 | 2.822 | 0.005 |
| d_energ_1 ~ energ_t1 | -0.173 | -0.286 | -0.059 | -0.299 | -2.986 | 0.003 |
| d_energ_1 ~~ sb07_03_t1 | 0.031 | -0.019 | 0.081 | 0.103 | 1.212 | 0.226 |
| sb07_03_t1 ~~ sb07_03_t1 | 1.539 | 1.321 | 1.757 | 1.000 | 13.834 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7601.961 | 7684.657 | 0.946 | 0.048 | 0.058 |
# parameters of interest
params_lcs_energ_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_energ_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sb07_03_t1", "sb07_03_t1 ~~ sb07_03_t1", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.926 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.641 | NA | NA |
| d_energ_1 ~1 | 0.379 | -0.126 | 0.884 | 1.443 | 1.473 | 0.141 |
| energ_t1 ~1 | 2.244 | 2.132 | 2.357 | 5.912 | 39.082 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.066 | 0.008 | 0.123 | 0.949 | 2.237 | 0.025 |
| d_energ_1 ~ energ_t1 | -0.157 | -0.390 | 0.077 | -0.226 | -1.316 | 0.188 |
| d_energ_1 ~~ sb07_03_t1 | 0.031 | -0.034 | 0.096 | 0.098 | 0.932 | 0.351 |
| sb07_03_t1 ~~ sb07_03_t1 | 1.539 | 1.321 | 1.757 | 1.000 | 13.834 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7717.247 | 7799.943 | 0.99 | 0.024 | 0.04 |
# parameters of interest
params_lcs_compa_curr_hyp4 <- broom::tidy(fit_mi_lcs_compa_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.149 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.351 | NA | NA |
| d_compa_1 ~1 | 0.791 | 0.277 | 1.305 | 3.762 | 3.014 | 0.003 |
| compa_t1 ~1 | 4.175 | 4.080 | 4.270 | 6.061 | 86.105 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.031 | -0.032 | 0.094 | 0.699 | 0.965 | 0.335 |
| d_compa_1 ~ compa_t1 | -0.168 | -0.286 | -0.049 | -0.549 | -2.760 | 0.006 |
| d_compa_1 ~~ sb06_01_t1 | 0.018 | -0.032 | 0.068 | 0.102 | 0.697 | 0.486 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.963 | 3.188 | 3.074 | 53.660 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 8030.888 | 8113.584 | 0.982 | 0.027 | 0.045 |
# parameters of interest
params_lcs_compa_ideal_hyp4 <- broom::tidy(fit_mi_lcs_compa_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.053 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.660 | NA | NA |
| d_compa_1 ~1 | 1.035 | 0.158 | 1.912 | 2.612 | 2.314 | 0.021 |
| compa_t1 ~1 | 4.207 | 4.109 | 4.305 | 6.653 | 84.403 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.133 | 0.048 | 0.218 | 0.847 | 3.080 | 0.002 |
| d_compa_1 ~ compa_t1 | -0.245 | -0.445 | -0.046 | -0.391 | -2.408 | 0.016 |
| d_compa_1 ~~ sb06_01_t1 | 0.015 | -0.067 | 0.096 | 0.040 | 0.351 | 0.725 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.963 | 3.188 | 3.074 | 53.657 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7917.002 | 7999.698 | 0.974 | 0.039 | 0.06 |
# parameters of interest
params_lcs_compa_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_compa_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sb07_04_t1", "sb07_04_t1 ~~ sb07_04_t1", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.154 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.356 | NA | NA |
| d_compa_1 ~1 | 0.810 | 0.285 | 1.334 | 3.834 | 3.026 | 0.002 |
| compa_t1 ~1 | 4.175 | 4.080 | 4.270 | 6.098 | 86.170 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.031 | -0.031 | 0.093 | 0.689 | 0.968 | 0.333 |
| d_compa_1 ~ compa_t1 | -0.172 | -0.294 | -0.050 | -0.558 | -2.768 | 0.006 |
| d_compa_1 ~~ sb07_04_t1 | -0.025 | -0.109 | 0.059 | -0.103 | -0.581 | 0.561 |
| sb07_04_t1 ~~ sb07_04_t1 | 1.927 | 1.727 | 2.126 | 1.000 | 18.947 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 8230.106 | 8312.802 | 0.981 | 0.028 | 0.046 |
# parameters of interest
params_lcs_compa_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_compa_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sb07_04_t1", "sb07_04_t1 ~~ sb07_04_t1", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.056 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.666 | NA | NA |
| d_compa_1 ~1 | 1.057 | 0.198 | 1.915 | 2.639 | 2.412 | 0.016 |
| compa_t1 ~1 | 4.207 | 4.109 | 4.305 | 6.624 | 84.339 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.135 | 0.051 | 0.219 | 0.842 | 3.134 | 0.002 |
| d_compa_1 ~ compa_t1 | -0.250 | -0.446 | -0.055 | -0.397 | -2.510 | 0.012 |
| d_compa_1 ~~ sb07_04_t1 | -0.042 | -0.156 | 0.072 | -0.083 | -0.725 | 0.468 |
| sb07_04_t1 ~~ sb07_04_t1 | 1.927 | 1.727 | 2.126 | 1.000 | 18.948 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7059.719 | 7142.415 | 0.954 | 0.063 | 0.054 |
# parameters of interest
params_lcs_respe_curr_hyp4 <- broom::tidy(fit_mi_lcs_respe_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.099 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.604 | NA | NA |
| d_respe_1 ~1 | 1.104 | 0.591 | 1.617 | 3.427 | 4.217 | 0.000 |
| respe_t1 ~1 | 4.391 | 4.309 | 4.474 | 7.499 | 104.075 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.084 | 0.044 | 0.125 | 0.814 | 4.084 | 0.000 |
| d_respe_1 ~ respe_t1 | -0.237 | -0.349 | -0.126 | -0.432 | -4.161 | 0.000 |
| d_respe_1 ~~ sb06_01_t1 | -0.040 | -0.093 | 0.012 | -0.139 | -1.500 | 0.134 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.852 | 1.149 | 1.000 | 13.225 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.187 | 3.074 | 53.680 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 6535.491 | 6618.187 | 0.932 | 0.065 | 0.061 |
# parameters of interest
params_lcs_respe_ideal_hyp4 <- broom::tidy(fit_mi_lcs_respe_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.162 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.715 | NA | NA |
| d_respe_1 ~1 | 1.502 | 0.653 | 2.351 | 5.600 | 3.467 | 0.001 |
| respe_t1 ~1 | 4.615 | 4.545 | 4.685 | 10.591 | 128.974 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.053 | 0.013 | 0.092 | 0.731 | 2.610 | 0.009 |
| d_respe_1 ~ respe_t1 | -0.319 | -0.498 | -0.140 | -0.519 | -3.491 | 0.000 |
| d_respe_1 ~~ sb06_01_t1 | -0.006 | -0.049 | 0.037 | -0.026 | -0.273 | 0.785 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.074 | 53.693 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7237.265 | 7319.961 | 0.905 | 0.092 | 0.103 |
# parameters of interest
params_lcs_respe_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_respe_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sb07_05_t1", "sb07_05_t1 ~~ sb07_05_t1", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.093 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.595 | NA | NA |
| d_respe_1 ~1 | 1.069 | 0.539 | 1.599 | 3.356 | 3.957 | 0.000 |
| respe_t1 ~1 | 4.391 | 4.309 | 4.474 | 7.500 | 104.062 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.083 | 0.043 | 0.124 | 0.822 | 4.046 | 0.000 |
| d_respe_1 ~ respe_t1 | -0.230 | -0.346 | -0.113 | -0.422 | -3.851 | 0.000 |
| d_respe_1 ~~ sb07_05_t1 | 0.009 | -0.070 | 0.087 | 0.023 | 0.220 | 0.826 |
| sb07_05_t1 ~~ sb07_05_t1 | 1.777 | 1.558 | 1.996 | 1.000 | 15.883 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 6706.441 | 6789.137 | 0.935 | 0.064 | 0.061 |
# parameters of interest
params_lcs_respe_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_respe_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sb07_05_t1", "sb07_05_t1 ~~ sb07_05_t1", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.170 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.729 | NA | NA |
| d_respe_1 ~1 | 1.547 | 0.699 | 2.395 | 5.697 | 3.575 | 0.000 |
| respe_t1 ~1 | 4.615 | 4.545 | 4.685 | 10.599 | 128.949 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.053 | 0.014 | 0.093 | 0.722 | 2.634 | 0.008 |
| d_respe_1 ~ respe_t1 | -0.329 | -0.508 | -0.150 | -0.528 | -3.598 | 0.000 |
| d_respe_1 ~~ sb07_05_t1 | -0.057 | -0.114 | 0.000 | -0.186 | -1.974 | 0.048 |
| sb07_05_t1 ~~ sb07_05_t1 | 1.777 | 1.558 | 1.996 | 1.000 | 15.884 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 8074.337 | 8157.033 | 0.946 | 0.062 | 0.049 |
# parameters of interest
params_lcs_trust_curr_hyp4 <- broom::tidy(fit_mi_lcs_trust_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.031 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.562 | NA | NA |
| d_trust_1 ~1 | 0.386 | -0.007 | 0.778 | 1.181 | 1.927 | 0.054 |
| trust_t1 ~1 | 2.893 | 2.772 | 3.014 | 4.825 | 46.995 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.095 | 0.016 | 0.175 | 0.894 | 2.348 | 0.019 |
| d_trust_1 ~ trust_t1 | -0.178 | -0.317 | -0.038 | -0.326 | -2.499 | 0.012 |
| d_trust_1 ~~ sb06_01_t1 | -0.020 | -0.080 | 0.041 | -0.063 | -0.636 | 0.525 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.224 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.964 | 3.188 | 3.075 | 53.685 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7643.513 | 7726.209 | 0.952 | 0.051 | 0.052 |
# parameters of interest
params_lcs_trust_ideal_hyp4 <- broom::tidy(fit_mi_lcs_trust_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.575 | NA | NA |
| d_trust_1 ~1 | 0.272 | -0.032 | 0.576 | 1.197 | 1.756 | 0.079 |
| trust_t1 ~1 | 1.957 | 1.865 | 2.050 | 5.051 | 41.380 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.048 | 0.009 | 0.088 | 0.933 | 2.386 | 0.017 |
| d_trust_1 ~ trust_t1 | -0.151 | -0.313 | 0.011 | -0.258 | -1.829 | 0.067 |
| d_trust_1 ~~ sb06_01_t1 | 0.002 | -0.039 | 0.044 | 0.011 | 0.115 | 0.908 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.074 | 53.696 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 8252.613 | 8335.309 | 0.938 | 0.067 | 0.057 |
# parameters of interest
params_lcs_trust_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_trust_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sb07_06_t1", "sb07_06_t1 ~~ sb07_06_t1", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.030 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.560 | NA | NA |
| d_trust_1 ~1 | 0.381 | -0.011 | 0.774 | 1.167 | 1.903 | 0.057 |
| trust_t1 ~1 | 2.893 | 2.773 | 3.014 | 4.811 | 47.028 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.095 | 0.016 | 0.175 | 0.894 | 2.341 | 0.019 |
| d_trust_1 ~ trust_t1 | -0.176 | -0.316 | -0.037 | -0.325 | -2.484 | 0.013 |
| d_trust_1 ~~ sb07_06_t1 | -0.013 | -0.096 | 0.070 | -0.032 | -0.314 | 0.753 |
| sb07_06_t1 ~~ sb07_06_t1 | 1.794 | 1.613 | 1.974 | 1.000 | 19.504 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7821.167 | 7903.863 | 0.942 | 0.057 | 0.052 |
# parameters of interest
params_lcs_trust_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_trust_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sb07_06_t1", "sb07_06_t1 ~~ sb07_06_t1", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.978 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.571 | NA | NA |
| d_trust_1 ~1 | 0.265 | -0.044 | 0.575 | 1.174 | 1.679 | 0.093 |
| trust_t1 ~1 | 1.957 | 1.864 | 2.050 | 5.064 | 41.378 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.048 | 0.008 | 0.087 | 0.936 | 2.368 | 0.018 |
| d_trust_1 ~ trust_t1 | -0.148 | -0.313 | 0.018 | -0.252 | -1.748 | 0.080 |
| d_trust_1 ~~ sb07_06_t1 | 0.016 | -0.045 | 0.076 | 0.054 | 0.515 | 0.607 |
| sb07_06_t1 ~~ sb07_06_t1 | 1.794 | 1.613 | 1.974 | 1.000 | 19.498 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7647.469 | 7730.165 | 0.981 | 0.051 | 0.048 |
# parameters of interest
params_lcs_organ_curr_hyp4 <- broom::tidy(fit_mi_lcs_organ_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.078 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.516 | NA | NA |
| d_organ_1 ~1 | 0.375 | 0.173 | 0.577 | 0.734 | 3.641 | 0.000 |
| organ_t1 ~1 | 2.819 | 2.671 | 2.967 | 2.640 | 37.419 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.222 | 0.122 | 0.323 | 0.852 | 4.351 | 0.000 |
| d_organ_1 ~ organ_t1 | -0.184 | -0.259 | -0.109 | -0.384 | -4.802 | 0.000 |
| d_organ_1 ~~ sb06_01_t1 | -0.067 | -0.149 | 0.016 | -0.141 | -1.580 | 0.114 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.150 | 1.000 | 13.220 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.074 | 53.686 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 6680.01 | 6762.705 | 0.931 | 0.054 | 0.057 |
# parameters of interest
params_lcs_organ_ideal_hyp4 <- broom::tidy(fit_mi_lcs_organ_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.071 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.816 | NA | NA |
| d_organ_1 ~1 | 0.559 | 0.291 | 0.827 | 1.570 | 4.092 | 0.000 |
| organ_t1 ~1 | 1.678 | 1.577 | 1.778 | 3.586 | 32.611 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.100 | 0.041 | 0.158 | 0.783 | 3.325 | 0.001 |
| d_organ_1 ~ organ_t1 | -0.355 | -0.520 | -0.189 | -0.465 | -4.195 | 0.000 |
| d_organ_1 ~~ sb06_01_t1 | -0.023 | -0.083 | 0.037 | -0.073 | -0.751 | 0.452 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.074 | 53.687 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7883.605 | 7966.301 | 0.881 | 0.132 | 0.155 |
# parameters of interest
params_lcs_organ_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_organ_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sb07_07_t1", "sb07_07_t1 ~~ sb07_07_t1", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.111 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.556 | NA | NA |
| d_organ_1 ~1 | 0.477 | 0.215 | 0.740 | 0.895 | 3.565 | 0.000 |
| organ_t1 ~1 | 2.819 | 2.671 | 2.966 | 2.644 | 37.420 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.229 | 0.128 | 0.331 | 0.806 | 4.416 | 0.000 |
| d_organ_1 ~ organ_t1 | -0.220 | -0.317 | -0.123 | -0.440 | -4.446 | 0.000 |
| d_organ_1 ~~ sb07_07_t1 | 0.077 | -0.063 | 0.217 | 0.110 | 1.076 | 0.282 |
| sb07_07_t1 ~~ sb07_07_t1 | 2.159 | 1.943 | 2.376 | 1.000 | 19.525 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 6913.735 | 6996.431 | 0.94 | 0.05 | 0.056 |
# parameters of interest
params_lcs_organ_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_organ_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sb07_07_t1", "sb07_07_t1 ~~ sb07_07_t1", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.068 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.816 | NA | NA |
| d_organ_1 ~1 | 0.559 | 0.292 | 0.826 | 1.580 | 4.102 | 0.000 |
| organ_t1 ~1 | 1.677 | 1.577 | 1.778 | 3.623 | 32.660 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.098 | 0.040 | 0.156 | 0.786 | 3.321 | 0.001 |
| d_organ_1 ~ organ_t1 | -0.354 | -0.519 | -0.189 | -0.463 | -4.196 | 0.000 |
| d_organ_1 ~~ sb07_07_t1 | 0.048 | -0.035 | 0.130 | 0.103 | 1.133 | 0.257 |
| sb07_07_t1 ~~ sb07_07_t1 | 2.159 | 1.943 | 2.376 | 1.000 | 19.536 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7606.687 | 7689.383 | 0.959 | 0.066 | 0.066 |
# parameters of interest
params_lcs_produ_curr_hyp4 <- broom::tidy(fit_mi_lcs_produ_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.045 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.484 | NA | NA |
| d_produ_1 ~1 | 0.336 | -0.002 | 0.674 | 0.957 | 1.948 | 0.051 |
| produ_t1 ~1 | 3.354 | 3.224 | 3.484 | 4.418 | 50.618 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.110 | 0.054 | 0.167 | 0.896 | 3.845 | 0.000 |
| d_produ_1 ~ produ_t1 | -0.149 | -0.251 | -0.048 | -0.323 | -2.880 | 0.004 |
| d_produ_1 ~~ sb06_01_t1 | 0.004 | -0.053 | 0.062 | 0.012 | 0.139 | 0.890 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.074 | 53.680 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 6226.929 | 6309.625 | 0.943 | 0.054 | 0.053 |
# parameters of interest
params_lcs_produ_ideal_hyp4 <- broom::tidy(fit_mi_lcs_produ_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.113 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.831 | NA | NA |
| d_produ_1 ~1 | 0.606 | 0.321 | 0.890 | 1.803 | 4.172 | 0.000 |
| produ_t1 ~1 | 1.630 | 1.544 | 1.717 | 3.621 | 37.055 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.084 | 0.023 | 0.146 | 0.747 | 2.680 | 0.007 |
| d_produ_1 ~ produ_t1 | -0.375 | -0.557 | -0.193 | -0.503 | -4.048 | 0.000 |
| d_produ_1 ~~ sb06_01_t1 | 0.004 | -0.051 | 0.059 | 0.014 | 0.142 | 0.887 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.074 | 53.695 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7763.107 | 7845.803 | 0.916 | 0.096 | 0.123 |
# parameters of interest
params_lcs_produ_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_produ_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sb07_08_t1", "sb07_08_t1 ~~ sb07_08_t1", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.055 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.496 | NA | NA |
| d_produ_1 ~1 | 0.376 | 0.007 | 0.745 | 1.057 | 1.998 | 0.046 |
| produ_t1 ~1 | 3.354 | 3.224 | 3.484 | 4.427 | 50.604 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.112 | 0.056 | 0.168 | 0.882 | 3.927 | 0.000 |
| d_produ_1 ~ produ_t1 | -0.161 | -0.273 | -0.050 | -0.343 | -2.845 | 0.004 |
| d_produ_1 ~~ sb07_08_t1 | 0.026 | -0.057 | 0.109 | 0.060 | 0.611 | 0.542 |
| sb07_08_t1 ~~ sb07_08_t1 | 1.673 | 1.417 | 1.930 | 1.000 | 12.796 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 6383.46 | 6466.156 | 0.953 | 0.049 | 0.053 |
# parameters of interest
params_lcs_produ_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_produ_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sb07_08_t1", "sb07_08_t1 ~~ sb07_08_t1", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.111 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.827 | NA | NA |
| d_produ_1 ~1 | 0.601 | 0.319 | 0.882 | 1.794 | 4.183 | 0.000 |
| produ_t1 ~1 | 1.630 | 1.544 | 1.717 | 3.623 | 37.071 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.084 | 0.022 | 0.146 | 0.750 | 2.672 | 0.008 |
| d_produ_1 ~ produ_t1 | -0.372 | -0.552 | -0.192 | -0.500 | -4.058 | 0.000 |
| d_produ_1 ~~ sb07_08_t1 | 0.016 | -0.046 | 0.077 | 0.042 | 0.498 | 0.618 |
| sb07_08_t1 ~~ sb07_08_t1 | 1.673 | 1.417 | 1.930 | 1.000 | 12.793 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7653.747 | 7736.443 | 0.92 | 0.083 | 0.084 |
# parameters of interest
params_lcs_respo_curr_hyp4 <- broom::tidy(fit_mi_lcs_respo_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.003 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.442 | NA | NA |
| d_respo_1 ~1 | 0.413 | 0.043 | 0.783 | 2.022 | 2.189 | 0.029 |
| respo_t1 ~1 | 3.619 | 3.517 | 3.721 | 7.811 | 69.694 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.040 | 0.004 | 0.075 | 0.948 | 2.173 | 0.030 |
| d_respo_1 ~ respo_t1 | -0.100 | -0.200 | -0.001 | -0.228 | -1.975 | 0.048 |
| d_respo_1 ~~ sb06_01_t1 | -0.036 | -0.078 | 0.007 | -0.179 | -1.647 | 0.100 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.077 | 2.965 | 3.189 | 3.076 | 53.742 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7089.666 | 7172.362 | 0.982 | 0.029 | 0.043 |
# parameters of interest
params_lcs_respo_ideal_hyp4 <- broom::tidy(fit_mi_lcs_respo_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.933 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.550 | NA | NA |
| d_respo_1 ~1 | 0.389 | -0.359 | 1.137 | 1.499 | 1.020 | 0.308 |
| respo_t1 ~1 | 4.259 | 4.151 | 4.366 | 9.670 | 77.659 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.065 | 0.018 | 0.113 | 0.971 | 2.697 | 0.007 |
| d_respo_1 ~ respo_t1 | -0.100 | -0.270 | 0.071 | -0.169 | -1.148 | 0.251 |
| d_respo_1 ~~ sb06_01_t1 | 0.003 | -0.052 | 0.058 | 0.010 | 0.095 | 0.925 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.074 | 53.678 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7896.304 | 7979 | 0.876 | 0.106 | 0.119 |
# parameters of interest
params_lcs_respo_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_respo_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sb07_09_t1", "sb07_09_t1 ~~ sb07_09_t1", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.018 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.461 | NA | NA |
| d_respo_1 ~1 | 0.485 | 0.097 | 0.874 | 2.311 | 2.451 | 0.014 |
| respo_t1 ~1 | 3.619 | 3.517 | 3.721 | 7.804 | 69.693 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.041 | 0.005 | 0.077 | 0.929 | 2.225 | 0.026 |
| d_respo_1 ~ respo_t1 | -0.120 | -0.226 | -0.015 | -0.266 | -2.242 | 0.025 |
| d_respo_1 ~~ sb07_09_t1 | -0.047 | -0.114 | 0.021 | -0.155 | -1.359 | 0.174 |
| sb07_09_t1 ~~ sb07_09_t1 | 2.210 | 2.019 | 2.400 | 1.000 | 22.757 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7331.213 | 7413.909 | 0.996 | 0.013 | 0.04 |
# parameters of interest
params_lcs_respo_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_respo_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sb07_09_t1", "sb07_09_t1 ~~ sb07_09_t1", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.933 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.551 | NA | NA |
| d_respo_1 ~1 | 0.390 | -0.362 | 1.143 | 1.506 | 1.017 | 0.309 |
| respo_t1 ~1 | 4.259 | 4.151 | 4.366 | 9.691 | 77.659 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.065 | 0.018 | 0.113 | 0.971 | 2.711 | 0.007 |
| d_respo_1 ~ respo_t1 | -0.100 | -0.271 | 0.071 | -0.169 | -1.143 | 0.253 |
| d_respo_1 ~~ sb07_09_t1 | -0.005 | -0.086 | 0.076 | -0.012 | -0.113 | 0.910 |
| sb07_09_t1 ~~ sb07_09_t1 | 2.210 | 2.019 | 2.400 | 1.000 | 22.758 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 8003.471 | 8086.166 | 0.964 | 0.055 | 0.066 |
# parameters of interest
params_lcs_anxie_curr_hyp4 <- broom::tidy(fit_mi_lcs_anxie_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.064 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.653 | NA | NA |
| d_anxie_1 ~1 | 0.908 | 0.582 | 1.235 | 1.606 | 5.453 | 0.000 |
| anxie_t1 ~1 | 2.778 | 2.643 | 2.913 | 3.015 | 40.335 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.268 | 0.125 | 0.411 | 0.838 | 3.679 | 0.000 |
| d_anxie_1 ~ anxie_t1 | -0.247 | -0.360 | -0.134 | -0.402 | -4.285 | 0.000 |
| d_anxie_1 ~~ sb06_01_t1 | 0.057 | -0.022 | 0.135 | 0.109 | 1.412 | 0.158 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.222 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.964 | 3.188 | 3.075 | 53.728 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7722.895 | 7805.591 | 1 | 0 | 0.049 |
# parameters of interest
params_lcs_anxie_ideal_hyp4 <- broom::tidy(fit_mi_lcs_anxie_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.205 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.768 | NA | NA |
| d_anxie_1 ~1 | 1.667 | 0.742 | 2.593 | 9.150 | 3.532 | 0.000 |
| anxie_t1 ~1 | 4.555 | 4.487 | 4.624 | 15.931 | 130.256 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.023 | -0.002 | 0.047 | 0.684 | 1.831 | 0.067 |
| d_anxie_1 ~ anxie_t1 | -0.358 | -0.558 | -0.158 | -0.562 | -3.514 | 0.000 |
| d_anxie_1 ~~ sb06_01_t1 | 0.010 | -0.022 | 0.043 | 0.069 | 0.629 | 0.530 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.222 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.074 | 53.689 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 8252.173 | 8334.869 | 0.865 | 0.113 | 0.138 |
# parameters of interest
params_lcs_anxie_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_anxie_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sb07_10_t1", "sb07_10_t1 ~~ sb07_10_t1", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.149 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.782 | NA | NA |
| d_anxie_1 ~1 | 1.203 | 0.814 | 1.592 | 1.918 | 6.068 | 0.000 |
| anxie_t1 ~1 | 2.779 | 2.644 | 2.914 | 3.018 | 40.385 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.288 | 0.143 | 0.432 | 0.731 | 3.897 | 0.000 |
| d_anxie_1 ~ anxie_t1 | -0.353 | -0.490 | -0.216 | -0.518 | -5.041 | 0.000 |
| d_anxie_1 ~~ sb07_10_t1 | -0.218 | -0.376 | -0.060 | -0.267 | -2.707 | 0.007 |
| sb07_10_t1 ~~ sb07_10_t1 | 2.315 | 2.071 | 2.558 | 1.000 | 18.644 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7978.25 | 8060.946 | 0.981 | 0.025 | 0.05 |
# parameters of interest
params_lcs_anxie_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_anxie_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sb07_10_t1", "sb07_10_t1 ~~ sb07_10_t1", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.197 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.755 | NA | NA |
| d_anxie_1 ~1 | 1.628 | 0.718 | 2.539 | 9.022 | 3.504 | 0.000 |
| anxie_t1 ~1 | 4.555 | 4.487 | 4.624 | 15.916 | 130.280 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.023 | -0.002 | 0.047 | 0.692 | 1.820 | 0.069 |
| d_anxie_1 ~ anxie_t1 | -0.350 | -0.547 | -0.153 | -0.555 | -3.486 | 0.000 |
| d_anxie_1 ~~ sb07_10_t1 | 0.021 | -0.023 | 0.064 | 0.090 | 0.930 | 0.353 |
| sb07_10_t1 ~~ sb07_10_t1 | 2.315 | 2.071 | 2.558 | 1.000 | 18.650 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7851.595 | 7934.291 | 0.926 | 0.101 | 0.107 |
# parameters of interest
params_lcs_depre_curr_hyp4 <- broom::tidy(fit_mi_lcs_depre_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.951 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.539 | NA | NA |
| d_depre_1 ~1 | 0.479 | 0.230 | 0.728 | 1.275 | 3.773 | 0.000 |
| depre_t1 ~1 | 2.931 | 2.807 | 3.055 | 4.423 | 46.361 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.136 | 0.076 | 0.196 | 0.964 | 4.420 | 0.000 |
| d_depre_1 ~ depre_t1 | -0.108 | -0.192 | -0.024 | -0.191 | -2.533 | 0.011 |
| d_depre_1 ~~ sb06_01_t1 | 0.021 | -0.031 | 0.074 | 0.058 | 0.794 | 0.427 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.964 | 3.188 | 3.074 | 53.709 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 6327.295 | 6409.991 | 0.911 | 0.068 | 0.065 |
# parameters of interest
params_lcs_depre_ideal_hyp4 <- broom::tidy(fit_mi_lcs_depre_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.926 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.795 | NA | NA |
| d_depre_1 ~1 | 1.262 | 0.201 | 2.324 | 4.040 | 2.330 | 0.020 |
| depre_t1 ~1 | 4.418 | 4.345 | 4.491 | 12.131 | 118.664 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.087 | 0.038 | 0.135 | 0.889 | 3.506 | 0.000 |
| d_depre_1 ~ depre_t1 | -0.285 | -0.520 | -0.051 | -0.333 | -2.382 | 0.017 |
| d_depre_1 ~~ sb06_01_t1 | 0.035 | -0.018 | 0.088 | 0.117 | 1.281 | 0.200 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.150 | 1.000 | 13.202 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.073 | 53.692 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 8123.727 | 8206.422 | 0.848 | 0.152 | 0.174 |
# parameters of interest
params_lcs_depre_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_depre_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sb07_11_t1", "sb07_11_t1 ~~ sb07_11_t1", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.035 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.652 | NA | NA |
| d_depre_1 ~1 | 0.842 | 0.477 | 1.207 | 2.025 | 4.522 | 0.00 |
| depre_t1 ~1 | 2.931 | 2.807 | 3.055 | 4.435 | 46.365 | 0.00 |
| d_depre_1 ~~ d_depre_1 | 0.150 | 0.085 | 0.214 | 0.864 | 4.529 | 0.00 |
| d_depre_1 ~ depre_t1 | -0.232 | -0.358 | -0.105 | -0.368 | -3.597 | 0.00 |
| d_depre_1 ~~ sb07_11_t1 | -0.172 | -0.302 | -0.042 | -0.281 | -2.591 | 0.01 |
| sb07_11_t1 ~~ sb07_11_t1 | 2.511 | 2.299 | 2.723 | 1.000 | 23.186 | 0.00 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 6607.643 | 6690.339 | 0.928 | 0.061 | 0.06 |
# parameters of interest
params_lcs_depre_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_depre_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sb07_11_t1", "sb07_11_t1 ~~ sb07_11_t1", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.922 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.784 | NA | NA |
| d_depre_1 ~1 | 1.210 | 0.177 | 2.242 | 3.871 | 2.296 | 0.022 |
| depre_t1 ~1 | 4.418 | 4.345 | 4.491 | 12.033 | 118.603 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.088 | 0.039 | 0.136 | 0.897 | 3.514 | 0.000 |
| d_depre_1 ~ depre_t1 | -0.273 | -0.502 | -0.045 | -0.321 | -2.349 | 0.019 |
| d_depre_1 ~~ sb07_11_t1 | -0.054 | -0.125 | 0.017 | -0.115 | -1.492 | 0.136 |
| sb07_11_t1 ~~ sb07_11_t1 | 2.510 | 2.297 | 2.722 | 1.000 | 23.159 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7988.185 | 8070.881 | 0.984 | 0.043 | 0.061 |
# parameters of interest
params_lcs_volat_curr_hyp4 <- broom::tidy(fit_mi_lcs_volat_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.076 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.605 | NA | NA |
| d_volat_1 ~1 | 0.529 | 0.275 | 0.783 | 1.054 | 4.075 | 0.000 |
| volat_t1 ~1 | 3.257 | 3.124 | 3.389 | 3.649 | 48.115 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.211 | 0.134 | 0.288 | 0.838 | 5.361 | 0.000 |
| d_volat_1 ~ volat_t1 | -0.226 | -0.302 | -0.150 | -0.402 | -5.816 | 0.000 |
| d_volat_1 ~~ sb06_01_t1 | 0.017 | -0.060 | 0.093 | 0.036 | 0.423 | 0.672 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.187 | 3.074 | 53.739 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7163.981 | 7246.676 | 0.909 | 0.065 | 0.065 |
# parameters of interest
params_lcs_volat_ideal_hyp4 <- broom::tidy(fit_mi_lcs_volat_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.946 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.585 | NA | NA |
| d_volat_1 ~1 | 0.221 | -0.050 | 0.492 | 0.725 | 1.599 | 0.110 |
| volat_t1 ~1 | 1.701 | 1.611 | 1.791 | 3.448 | 36.978 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.089 | 0.031 | 0.146 | 0.954 | 3.024 | 0.002 |
| d_volat_1 ~ volat_t1 | -0.132 | -0.303 | 0.039 | -0.214 | -1.516 | 0.130 |
| d_volat_1 ~~ sb06_01_t1 | -0.043 | -0.104 | 0.018 | -0.143 | -1.373 | 0.170 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.964 | 3.188 | 3.075 | 53.695 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 8149.41 | 8232.106 | 0.962 | 0.066 | 0.093 |
# parameters of interest
params_lcs_volat_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_volat_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sb07_12_t1", "sb07_12_t1 ~~ sb07_12_t1", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.071 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.599 | NA | NA |
| d_volat_1 ~1 | 0.509 | 0.238 | 0.780 | 1.020 | 3.685 | 0.000 |
| volat_t1 ~1 | 3.257 | 3.124 | 3.389 | 3.650 | 48.115 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.210 | 0.132 | 0.288 | 0.845 | 5.281 | 0.000 |
| d_volat_1 ~ volat_t1 | -0.220 | -0.302 | -0.138 | -0.394 | -5.275 | 0.000 |
| d_volat_1 ~~ sb07_12_t1 | -0.007 | -0.114 | 0.100 | -0.012 | -0.128 | 0.898 |
| sb07_12_t1 ~~ sb07_12_t1 | 1.697 | 1.505 | 1.889 | 1.000 | 17.292 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7326.795 | 7409.491 | 0.895 | 0.07 | 0.068 |
# parameters of interest
params_lcs_volat_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_volat_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sb07_12_t1", "sb07_12_t1 ~~ sb07_12_t1", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.939 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.574 | NA | NA |
| d_volat_1 ~1 | 0.201 | -0.072 | 0.474 | 0.667 | 1.440 | 0.150 |
| volat_t1 ~1 | 1.701 | 1.611 | 1.791 | 3.456 | 36.997 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.087 | 0.030 | 0.144 | 0.961 | 2.987 | 0.003 |
| d_volat_1 ~ volat_t1 | -0.120 | -0.292 | 0.052 | -0.196 | -1.370 | 0.171 |
| d_volat_1 ~~ sb07_12_t1 | -0.022 | -0.096 | 0.053 | -0.056 | -0.567 | 0.571 |
| sb07_12_t1 ~~ sb07_12_t1 | 1.697 | 1.505 | 1.889 | 1.000 | 17.293 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7756.127 | 7838.823 | 0.974 | 0.042 | 0.049 |
# parameters of interest
params_lcs_curio_curr_hyp4 <- broom::tidy(fit_mi_lcs_curio_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.026 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.351 | NA | NA |
| d_curio_1 ~1 | 0.402 | -0.082 | 0.885 | 2.407 | 1.628 | 0.103 |
| curio_t1 ~1 | 4.065 | 3.968 | 4.162 | 8.337 | 82.261 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.026 | -0.017 | 0.069 | 0.940 | 1.191 | 0.234 |
| d_curio_1 ~ curio_t1 | -0.084 | -0.200 | 0.033 | -0.245 | -1.406 | 0.160 |
| d_curio_1 ~~ sb06_01_t1 | -0.007 | -0.060 | 0.046 | -0.043 | -0.258 | 0.797 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.222 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.074 | 53.631 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7748.924 | 7831.62 | 0.971 | 0.036 | 0.052 |
# parameters of interest
params_lcs_curio_ideal_hyp4 <- broom::tidy(fit_mi_lcs_curio_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.973 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.205 | NA | NA |
| d_curio_1 ~1 | -0.037 | -1.042 | 0.968 | -0.488 | -0.072 | 0.942 |
| curio_t1 ~1 | 4.427 | 4.350 | 4.504 | 12.279 | 113.040 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.006 | -0.035 | 0.046 | 0.999 | 0.279 | 0.780 |
| d_curio_1 ~ curio_t1 | 0.006 | -0.217 | 0.228 | 0.027 | 0.050 | 0.960 |
| d_curio_1 ~~ sb06_01_t1 | -0.010 | -0.059 | 0.040 | -0.129 | -0.385 | 0.700 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.225 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.074 | 53.682 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7895.012 | 7977.708 | 0.953 | 0.057 | 0.067 |
# parameters of interest
params_lcs_curio_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_curio_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sb07_13_t1", "sb07_13_t1 ~~ sb07_13_t1", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.034 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.365 | NA | NA |
| d_curio_1 ~1 | 0.445 | -0.072 | 0.963 | 2.614 | 1.687 | 0.092 |
| curio_t1 ~1 | 4.065 | 3.968 | 4.162 | 8.431 | 82.329 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.027 | -0.016 | 0.070 | 0.928 | 1.221 | 0.222 |
| d_curio_1 ~ curio_t1 | -0.095 | -0.221 | 0.031 | -0.268 | -1.470 | 0.141 |
| d_curio_1 ~~ sb07_13_t1 | -0.023 | -0.095 | 0.049 | -0.112 | -0.625 | 0.532 |
| sb07_13_t1 ~~ sb07_13_t1 | 1.581 | 1.405 | 1.757 | 1.000 | 17.614 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7888.314 | 7971.01 | 0.992 | 0.019 | 0.04 |
# parameters of interest
params_lcs_curio_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_curio_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sb07_13_t1", "sb07_13_t1 ~~ sb07_13_t1", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.975 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.209 | NA | NA |
| d_curio_1 ~1 | -0.027 | -0.981 | 0.928 | -0.342 | -0.055 | 0.956 |
| curio_t1 ~1 | 4.427 | 4.350 | 4.504 | 12.206 | 113.041 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.006 | -0.035 | 0.047 | 1.000 | 0.293 | 0.769 |
| d_curio_1 ~ curio_t1 | 0.003 | -0.208 | 0.215 | 0.015 | 0.031 | 0.975 |
| d_curio_1 ~~ sb07_13_t1 | 0.012 | -0.053 | 0.078 | 0.126 | 0.369 | 0.712 |
| sb07_13_t1 ~~ sb07_13_t1 | 1.581 | 1.405 | 1.757 | 1.000 | 17.620 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7983.746 | 8066.442 | 0.993 | 0.026 | 0.037 |
# parameters of interest
params_lcs_aesth_curr_hyp4 <- broom::tidy(fit_mi_lcs_aesth_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.978 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.510 | NA | NA |
| d_aesth_1 ~1 | 0.341 | 0.022 | 0.661 | 13.207 | 2.092 | 0.036 |
| aesth_t1 ~1 | 2.990 | 2.870 | 3.110 | 60.367 | 48.915 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 0.953 | 4.475 | 0.000 |
| d_aesth_1 ~ aesth_t1 | -0.113 | -0.220 | -0.007 | -0.217 | -2.088 | 0.037 |
| d_aesth_1 ~~ sb06_01_t1 | -0.001 | -0.005 | 0.003 | -0.039 | -0.466 | 0.642 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.222 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.187 | 3.074 | 53.686 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7818.445 | 7901.141 | 1 | 0 | 0.044 |
# parameters of interest
params_lcs_aesth_ideal_hyp4 <- broom::tidy(fit_mi_lcs_aesth_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.932 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.561 | NA | NA |
| d_aesth_1 ~1 | 0.323 | -0.116 | 0.761 | 3.789 | 1.443 | 0.149 |
| aesth_t1 ~1 | 3.126 | 2.993 | 3.258 | 22.111 | 46.237 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.007 | -0.006 | 0.020 | 0.969 | 1.042 | 0.297 |
| d_aesth_1 ~ aesth_t1 | -0.105 | -0.244 | 0.033 | -0.175 | -1.495 | 0.135 |
| d_aesth_1 ~~ sb06_01_t1 | -0.008 | -0.026 | 0.009 | -0.100 | -0.961 | 0.337 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.150 | 1.000 | 13.227 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.187 | 3.073 | 53.653 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 8169.366 | 8252.062 | 0.993 | 0.027 | 0.041 |
# parameters of interest
params_lcs_aesth_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_aesth_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sb07_14_t1", "sb07_14_t1 ~~ sb07_14_t1", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.512 | NA | NA |
| d_aesth_1 ~1 | 0.349 | 0.034 | 0.663 | 12.295 | 2.175 | 0.030 |
| aesth_t1 ~1 | 2.990 | 2.870 | 3.110 | 55.029 | 48.910 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 0.951 | 4.478 | 0.000 |
| d_aesth_1 ~ aesth_t1 | -0.116 | -0.220 | -0.011 | -0.222 | -2.170 | 0.030 |
| d_aesth_1 ~~ sb07_14_t1 | 0.006 | 0.000 | 0.012 | 0.164 | 1.953 | 0.051 |
| sb07_14_t1 ~~ sb07_14_t1 | 1.863 | 1.683 | 2.043 | 1.000 | 20.241 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 8004.787 | 8087.483 | 0.996 | 0.018 | 0.05 |
# parameters of interest
params_lcs_aesth_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_aesth_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sb07_14_t1", "sb07_14_t1 ~~ sb07_14_t1", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.947 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.582 | NA | NA |
| d_aesth_1 ~1 | 0.403 | -0.028 | 0.835 | 4.709 | 1.832 | 0.067 |
| aesth_t1 ~1 | 3.126 | 2.993 | 3.258 | 22.398 | 46.239 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.007 | -0.006 | 0.020 | 0.954 | 1.027 | 0.304 |
| d_aesth_1 ~ aesth_t1 | -0.131 | -0.268 | 0.005 | -0.214 | -1.888 | 0.059 |
| d_aesth_1 ~~ sb07_14_t1 | 0.020 | -0.009 | 0.049 | 0.176 | 1.352 | 0.176 |
| sb07_14_t1 ~~ sb07_14_t1 | 1.864 | 1.683 | 2.044 | 1.000 | 20.230 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7571.494 | 7654.189 | 0.975 | 0.048 | 0.054 |
# parameters of interest
params_lcs_imagi_curr_hyp4 <- broom::tidy(fit_mi_lcs_imagi_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.989 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.496 | NA | NA |
| d_imagi_1 ~1 | 0.492 | 0.053 | 0.931 | 1.256 | 2.195 | 0.028 |
| imagi_t1 ~1 | 3.550 | 3.441 | 3.659 | 4.546 | 63.926 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.145 | 0.062 | 0.228 | 0.948 | 3.429 | 0.001 |
| d_imagi_1 ~ imagi_t1 | -0.115 | -0.234 | 0.004 | -0.229 | -1.888 | 0.059 |
| d_imagi_1 ~~ sb06_01_t1 | 0.009 | -0.057 | 0.076 | 0.025 | 0.280 | 0.780 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.223 | 0.000 |
| sb06_01_t1 ~1 | 3.076 | 2.963 | 3.188 | 3.074 | 53.670 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7036.507 | 7119.202 | 0.911 | 0.063 | 0.063 |
# parameters of interest
params_lcs_imagi_ideal_hyp4 <- broom::tidy(fit_mi_lcs_imagi_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.987 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.875 | NA | NA |
| d_imagi_1 ~1 | 1.740 | 0.500 | 2.980 | 4.061 | 2.751 | 0.006 |
| imagi_t1 ~1 | 4.571 | 4.502 | 4.640 | 9.462 | 130.008 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.150 | 0.073 | 0.226 | 0.817 | 3.837 | 0.000 |
| d_imagi_1 ~ imagi_t1 | -0.380 | -0.645 | -0.115 | -0.428 | -2.807 | 0.005 |
| d_imagi_1 ~~ sb06_01_t1 | 0.013 | -0.050 | 0.077 | 0.035 | 0.414 | 0.679 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.001 | 0.853 | 1.149 | 1.000 | 13.222 | 0.000 |
| sb06_01_t1 ~1 | 3.075 | 2.963 | 3.188 | 3.074 | 53.689 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7772.139 | 7854.835 | 0.952 | 0.068 | 0.08 |
# parameters of interest
params_lcs_imagi_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_imagi_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sb07_15_t1", "sb07_15_t1 ~~ sb07_15_t1", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.984 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.490 | NA | NA |
| d_imagi_1 ~1 | 0.468 | 0.031 | 0.905 | 1.201 | 2.097 | 0.036 |
| imagi_t1 ~1 | 3.550 | 3.441 | 3.659 | 4.543 | 63.898 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.144 | 0.061 | 0.228 | 0.953 | 3.403 | 0.001 |
| d_imagi_1 ~ imagi_t1 | -0.108 | -0.226 | 0.011 | -0.216 | -1.785 | 0.074 |
| d_imagi_1 ~~ sb07_15_t1 | 0.040 | -0.054 | 0.135 | 0.076 | 0.834 | 0.404 |
| sb07_15_t1 ~~ sb07_15_t1 | 1.959 | 1.770 | 2.147 | 1.000 | 20.365 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 22 | 7237.889 | 7320.585 | 0.907 | 0.064 | 0.064 |
# parameters of interest
params_lcs_imagi_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_imagi_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sb07_15_t1", "sb07_15_t1 ~~ sb07_15_t1", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.984 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.868 | NA | NA |
| d_imagi_1 ~1 | 1.709 | 0.465 | 2.953 | 4.012 | 2.692 | 0.007 |
| imagi_t1 ~1 | 4.571 | 4.502 | 4.640 | 9.469 | 130.136 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.149 | 0.074 | 0.225 | 0.821 | 3.868 | 0.000 |
| d_imagi_1 ~ imagi_t1 | -0.373 | -0.639 | -0.107 | -0.423 | -2.745 | 0.006 |
| d_imagi_1 ~~ sb07_15_t1 | -0.013 | -0.098 | 0.071 | -0.025 | -0.309 | 0.757 |
| sb07_15_t1 ~~ sb07_15_t1 | 1.959 | 1.770 | 2.147 | 1.000 | 20.366 | 0.000 |
Run models for all facets with a template & loop:
# create df for table:
df_table_hyp4 <- bind_rows(
#traits
params_lcs_extra_curr_hyp4 %>% filter(term=="d_extra_1 ~~ sb06_01_t1"),
params_lcs_extra_ideal_hyp4 %>% filter(term=="d_extra_1 ~~ sb06_01_t1"),
params_lcs_extra_curr_specif_hyp4 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_extra_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_agree_curr_hyp4 %>% filter(term=="d_agree_1 ~~ sb06_01_t1"),
params_lcs_agree_ideal_hyp4 %>% filter(term=="d_agree_1 ~~ sb06_01_t1"),
params_lcs_agree_curr_specif_hyp4 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_agree_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_consc_curr_hyp4 %>% filter(term=="d_consc_1 ~~ sb06_01_t1"),
params_lcs_consc_ideal_hyp4 %>% filter(term=="d_consc_1 ~~ sb06_01_t1"),
params_lcs_consc_curr_specif_hyp4 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_consc_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_neuro_curr_hyp4 %>% filter(term=="d_neuro_1 ~~ sb06_01_t1"),
params_lcs_neuro_ideal_hyp4 %>% filter(term=="d_neuro_1 ~~ sb06_01_t1"),
params_lcs_neuro_curr_specif_hyp4 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_neuro_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_openn_curr_hyp4 %>% filter(term=="d_openn_1 ~~ sb06_01_t1"),
params_lcs_openn_ideal_hyp4 %>% filter(term=="d_openn_1 ~~ sb06_01_t1"),
params_lcs_openn_curr_specif_hyp4 %>% filter(term=="goals ~~ d_openn_1"),
params_lcs_openn_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_openn_1"),
#facets
params_lcs_socia_curr_hyp4 %>% filter(term=="d_socia_1 ~~ sb06_01_t1"),
params_lcs_socia_ideal_hyp4 %>% filter(term=="d_socia_1 ~~ sb06_01_t1"),
params_lcs_socia_curr_specif_hyp4 %>% filter(term=="d_socia_1 ~~ sb07_01_t1"),
params_lcs_socia_ideal_specif_hyp4 %>% filter(term=="d_socia_1 ~~ sb07_01_t1"),
params_lcs_asser_curr_hyp4 %>% filter(term=="d_asser_1 ~~ sb06_01_t1"),
params_lcs_asser_ideal_hyp4 %>% filter(term=="d_asser_1 ~~ sb06_01_t1"),
params_lcs_asser_curr_specif_hyp4 %>% filter(term=="d_asser_1 ~~ sb07_02_t1"),
params_lcs_asser_ideal_specif_hyp4 %>% filter(term=="d_asser_1 ~~ sb07_02_t1"),
params_lcs_energ_curr_hyp4 %>% filter(term=="d_energ_1 ~~ sb06_01_t1"),
params_lcs_energ_ideal_hyp4 %>% filter(term=="d_energ_1 ~~ sb06_01_t1"),
params_lcs_energ_curr_specif_hyp4 %>% filter(term=="d_energ_1 ~~ sb07_03_t1"),
params_lcs_energ_ideal_specif_hyp4 %>% filter(term=="d_energ_1 ~~ sb07_03_t1"),
params_lcs_compa_curr_hyp4 %>% filter(term=="d_compa_1 ~~ sb06_01_t1"),
params_lcs_compa_ideal_hyp4 %>% filter(term=="d_compa_1 ~~ sb06_01_t1"),
params_lcs_compa_curr_specif_hyp4 %>% filter(term=="d_compa_1 ~~ sb07_04_t1"),
params_lcs_compa_ideal_specif_hyp4 %>% filter(term=="d_compa_1 ~~ sb07_04_t1"),
params_lcs_respe_curr_hyp4 %>% filter(term=="d_respe_1 ~~ sb06_01_t1"),
params_lcs_respe_ideal_hyp4 %>% filter(term=="d_respe_1 ~~ sb06_01_t1"),
params_lcs_respe_curr_specif_hyp4 %>% filter(term=="d_respe_1 ~~ sb07_05_t1"),
params_lcs_respe_ideal_specif_hyp4 %>% filter(term=="d_respe_1 ~~ sb07_05_t1"),
params_lcs_trust_curr_hyp4 %>% filter(term=="d_trust_1 ~~ sb06_01_t1"),
params_lcs_trust_ideal_hyp4 %>% filter(term=="d_trust_1 ~~ sb06_01_t1"),
params_lcs_trust_curr_specif_hyp4 %>% filter(term=="d_trust_1 ~~ sb07_06_t1"),
params_lcs_trust_ideal_specif_hyp4 %>% filter(term=="d_trust_1 ~~ sb07_06_t1"),
params_lcs_organ_curr_hyp4 %>% filter(term=="d_organ_1 ~~ sb06_01_t1"),
params_lcs_organ_ideal_hyp4 %>% filter(term=="d_organ_1 ~~ sb06_01_t1"),
params_lcs_organ_curr_specif_hyp4 %>% filter(term=="d_organ_1 ~~ sb07_07_t1"),
params_lcs_organ_ideal_specif_hyp4 %>% filter(term=="d_organ_1 ~~ sb07_07_t1"),
params_lcs_produ_curr_hyp4 %>% filter(term=="d_produ_1 ~~ sb06_01_t1"),
params_lcs_produ_ideal_hyp4 %>% filter(term=="d_produ_1 ~~ sb06_01_t1"),
params_lcs_produ_curr_specif_hyp4 %>% filter(term=="d_produ_1 ~~ sb07_08_t1"),
params_lcs_produ_ideal_specif_hyp4 %>% filter(term=="d_produ_1 ~~ sb07_08_t1"),
params_lcs_respo_curr_hyp4 %>% filter(term=="d_respo_1 ~~ sb06_01_t1"),
params_lcs_respo_ideal_hyp4 %>% filter(term=="d_respo_1 ~~ sb06_01_t1"),
params_lcs_respo_curr_specif_hyp4 %>% filter(term=="d_respo_1 ~~ sb07_09_t1"),
params_lcs_respo_ideal_specif_hyp4 %>% filter(term=="d_respo_1 ~~ sb07_09_t1"),
params_lcs_anxie_curr_hyp4 %>% filter(term=="d_anxie_1 ~~ sb06_01_t1"),
params_lcs_anxie_ideal_hyp4 %>% filter(term=="d_anxie_1 ~~ sb06_01_t1"),
params_lcs_anxie_curr_specif_hyp4 %>% filter(term=="d_anxie_1 ~~ sb07_10_t1"),
params_lcs_anxie_ideal_specif_hyp4 %>% filter(term=="d_anxie_1 ~~ sb07_10_t1"),
params_lcs_depre_curr_hyp4 %>% filter(term=="d_depre_1 ~~ sb06_01_t1"),
params_lcs_depre_ideal_hyp4 %>% filter(term=="d_depre_1 ~~ sb06_01_t1"),
params_lcs_depre_curr_specif_hyp4 %>% filter(term=="d_depre_1 ~~ sb07_11_t1"),
params_lcs_depre_ideal_specif_hyp4 %>% filter(term=="d_depre_1 ~~ sb07_11_t1"),
params_lcs_volat_curr_hyp4 %>% filter(term=="d_volat_1 ~~ sb06_01_t1"),
params_lcs_volat_ideal_hyp4 %>% filter(term=="d_volat_1 ~~ sb06_01_t1"),
params_lcs_volat_curr_specif_hyp4 %>% filter(term=="d_volat_1 ~~ sb07_12_t1"),
params_lcs_volat_ideal_specif_hyp4 %>% filter(term=="d_volat_1 ~~ sb07_12_t1"),
params_lcs_curio_curr_hyp4 %>% filter(term=="d_curio_1 ~~ sb06_01_t1"),
params_lcs_curio_ideal_hyp4 %>% filter(term=="d_curio_1 ~~ sb06_01_t1"),
params_lcs_curio_curr_specif_hyp4 %>% filter(term=="d_curio_1 ~~ sb07_13_t1"),
params_lcs_curio_ideal_specif_hyp4 %>% filter(term=="d_curio_1 ~~ sb07_13_t1"),
params_lcs_aesth_curr_hyp4 %>% filter(term=="d_aesth_1 ~~ sb06_01_t1"),
params_lcs_aesth_ideal_hyp4 %>% filter(term=="d_aesth_1 ~~ sb06_01_t1"),
params_lcs_aesth_curr_specif_hyp4 %>% filter(term=="d_aesth_1 ~~ sb07_14_t1"),
params_lcs_aesth_ideal_specif_hyp4 %>% filter(term=="d_aesth_1 ~~ sb07_14_t1"),
params_lcs_imagi_curr_hyp4 %>% filter(term=="d_imagi_1 ~~ sb06_01_t1"),
params_lcs_imagi_ideal_hyp4 %>% filter(term=="d_imagi_1 ~~ sb06_01_t1"),
params_lcs_imagi_curr_specif_hyp4 %>% filter(term=="d_imagi_1 ~~ sb07_15_t1"),
params_lcs_imagi_ideal_specif_hyp4 %>% filter(term=="d_imagi_1 ~~ sb07_15_t1"),
) %>%
mutate(trait = rep(names(b5_vars), each=4),
ref = rep(rep(c("current", "ideal"), 2), 20),
goal = rep(c(rep("general", 2), rep("specific", 2)), 20)) %>%
select(trait, ref, goal, estimate, std.all, statistic, p.value)Results summary across the Big Five traits: covariance of the latent change score and change goal(s)
kable(df_table_hyp4[1:20, ], digits = 3)| trait | ref | goal | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extraversion | current | general | 0.052 | 0.163 | 2.006 | 0.045 |
| extraversion | ideal | general | 0.035 | 0.135 | 1.673 | 0.094 |
| extraversion | current | specific | 0.008 | 0.044 | 0.374 | 0.708 |
| extraversion | ideal | specific | 0.014 | 0.096 | 0.630 | 0.528 |
| agreeableness | current | general | -0.018 | -0.087 | -1.006 | 0.314 |
| agreeableness | ideal | general | -0.002 | -0.009 | -0.098 | 0.922 |
| agreeableness | current | specific | 0.001 | 0.006 | 0.056 | 0.955 |
| agreeableness | ideal | specific | -0.035 | -0.148 | -1.459 | 0.145 |
| conscientiousness | current | general | 0.011 | 0.037 | 0.484 | 0.628 |
| conscientiousness | ideal | general | 0.011 | 0.040 | 0.447 | 0.655 |
| conscientiousness | current | specific | -0.003 | -0.009 | -0.093 | 0.926 |
| conscientiousness | ideal | specific | -0.016 | -0.051 | -0.603 | 0.546 |
| neuroticism | current | general | -0.045 | -0.096 | -1.407 | 0.159 |
| neuroticism | ideal | general | -0.027 | -0.084 | -1.033 | 0.302 |
| neuroticism | current | specific | 0.121 | 0.229 | 2.218 | 0.027 |
| neuroticism | ideal | specific | 0.001 | 0.003 | 0.043 | 0.966 |
| openness | current | general | -0.012 | -0.061 | -0.594 | 0.552 |
| openness | ideal | general | -0.028 | -0.115 | -1.154 | 0.249 |
| openness | current | specific | 0.029 | 0.247 | 2.221 | 0.026 |
| openness | ideal | specific | 0.015 | 0.097 | 0.993 | 0.321 |
Results summary across the Big Five facets: covariance of the latent change score and change goal(s)
kable(df_table_hyp4[21:80, ], digits = 3)| trait | ref | goal | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| sociability | current | general | 0.081 | 0.162 | 1.983 | 0.047 |
| sociability | ideal | general | 0.042 | 0.187 | 1.540 | 0.124 |
| sociability | current | specific | -0.058 | -0.097 | -1.197 | 0.231 |
| sociability | ideal | specific | 0.062 | 0.228 | 1.959 | 0.050 |
| assertiveness | current | general | 0.010 | 0.049 | 0.328 | 0.743 |
| assertiveness | ideal | general | 0.037 | 0.201 | 1.876 | 0.061 |
| assertiveness | current | specific | 0.014 | 0.060 | 0.440 | 0.660 |
| assertiveness | ideal | specific | 0.018 | 0.082 | 0.807 | 0.420 |
| energy | current | general | -0.018 | -0.076 | -0.808 | 0.419 |
| energy | ideal | general | -0.031 | -0.119 | -1.136 | 0.256 |
| energy | current | specific | 0.031 | 0.103 | 1.212 | 0.226 |
| energy | ideal | specific | 0.031 | 0.098 | 0.932 | 0.351 |
| compassion | current | general | 0.018 | 0.102 | 0.697 | 0.486 |
| compassion | ideal | general | 0.015 | 0.040 | 0.351 | 0.725 |
| compassion | current | specific | -0.025 | -0.103 | -0.581 | 0.561 |
| compassion | ideal | specific | -0.042 | -0.083 | -0.725 | 0.468 |
| respectfulness | current | general | -0.040 | -0.139 | -1.500 | 0.134 |
| respectfulness | ideal | general | -0.006 | -0.026 | -0.273 | 0.785 |
| respectfulness | current | specific | 0.009 | 0.023 | 0.220 | 0.826 |
| respectfulness | ideal | specific | -0.057 | -0.186 | -1.974 | 0.048 |
| trust | current | general | -0.020 | -0.063 | -0.636 | 0.525 |
| trust | ideal | general | 0.002 | 0.011 | 0.115 | 0.908 |
| trust | current | specific | -0.013 | -0.032 | -0.314 | 0.753 |
| trust | ideal | specific | 0.016 | 0.054 | 0.515 | 0.607 |
| organization | current | general | -0.067 | -0.141 | -1.580 | 0.114 |
| organization | ideal | general | -0.023 | -0.073 | -0.751 | 0.452 |
| organization | current | specific | 0.077 | 0.110 | 1.076 | 0.282 |
| organization | ideal | specific | 0.048 | 0.103 | 1.133 | 0.257 |
| productiveness | current | general | 0.004 | 0.012 | 0.139 | 0.890 |
| productiveness | ideal | general | 0.004 | 0.014 | 0.142 | 0.887 |
| productiveness | current | specific | 0.026 | 0.060 | 0.611 | 0.542 |
| productiveness | ideal | specific | 0.016 | 0.042 | 0.498 | 0.618 |
| responsibility | current | general | -0.036 | -0.179 | -1.647 | 0.100 |
| responsibility | ideal | general | 0.003 | 0.010 | 0.095 | 0.925 |
| responsibility | current | specific | -0.047 | -0.155 | -1.359 | 0.174 |
| responsibility | ideal | specific | -0.005 | -0.012 | -0.113 | 0.910 |
| anxiety | current | general | 0.057 | 0.109 | 1.412 | 0.158 |
| anxiety | ideal | general | 0.010 | 0.069 | 0.629 | 0.530 |
| anxiety | current | specific | -0.218 | -0.267 | -2.707 | 0.007 |
| anxiety | ideal | specific | 0.021 | 0.090 | 0.930 | 0.353 |
| depression | current | general | 0.021 | 0.058 | 0.794 | 0.427 |
| depression | ideal | general | 0.035 | 0.117 | 1.281 | 0.200 |
| depression | current | specific | -0.172 | -0.281 | -2.591 | 0.010 |
| depression | ideal | specific | -0.054 | -0.115 | -1.492 | 0.136 |
| volatility | current | general | 0.017 | 0.036 | 0.423 | 0.672 |
| volatility | ideal | general | -0.043 | -0.143 | -1.373 | 0.170 |
| volatility | current | specific | -0.007 | -0.012 | -0.128 | 0.898 |
| volatility | ideal | specific | -0.022 | -0.056 | -0.567 | 0.571 |
| curiosity | current | general | -0.007 | -0.043 | -0.258 | 0.797 |
| curiosity | ideal | general | -0.010 | -0.129 | -0.385 | 0.700 |
| curiosity | current | specific | -0.023 | -0.112 | -0.625 | 0.532 |
| curiosity | ideal | specific | 0.012 | 0.126 | 0.369 | 0.712 |
| aesthetic | current | general | -0.001 | -0.039 | -0.466 | 0.642 |
| aesthetic | ideal | general | -0.008 | -0.100 | -0.961 | 0.337 |
| aesthetic | current | specific | 0.006 | 0.164 | 1.953 | 0.051 |
| aesthetic | ideal | specific | 0.020 | 0.176 | 1.352 | 0.176 |
| imagination | current | general | 0.009 | 0.025 | 0.280 | 0.780 |
| imagination | ideal | general | 0.013 | 0.035 | 0.414 | 0.679 |
| imagination | current | specific | 0.040 | 0.076 | 0.834 | 0.404 |
| imagination | ideal | specific | -0.013 | -0.025 | -0.309 | 0.757 |
Prepare data frame for plotting:
df_table_hyp4_plot <- bind_rows(
#traits
params_lcs_extra_curr_hyp4 %>% filter(term=="d_extra_1 ~~ sb06_01_t1"),
params_lcs_extra_ideal_hyp4 %>% filter(term=="d_extra_1 ~~ sb06_01_t1"),
params_lcs_extra_curr_specif_hyp4 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_extra_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_agree_curr_hyp4 %>% filter(term=="d_agree_1 ~~ sb06_01_t1"),
params_lcs_agree_ideal_hyp4 %>% filter(term=="d_agree_1 ~~ sb06_01_t1"),
params_lcs_agree_curr_specif_hyp4 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_agree_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_consc_curr_hyp4 %>% filter(term=="d_consc_1 ~~ sb06_01_t1"),
params_lcs_consc_ideal_hyp4 %>% filter(term=="d_consc_1 ~~ sb06_01_t1"),
params_lcs_consc_curr_specif_hyp4 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_consc_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_neuro_curr_hyp4 %>% filter(term=="d_neuro_1 ~~ sb06_01_t1"),
params_lcs_neuro_ideal_hyp4 %>% filter(term=="d_neuro_1 ~~ sb06_01_t1"),
params_lcs_neuro_curr_specif_hyp4 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_neuro_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_openn_curr_hyp4 %>% filter(term=="d_openn_1 ~~ sb06_01_t1"),
params_lcs_openn_ideal_hyp4 %>% filter(term=="d_openn_1 ~~ sb06_01_t1"),
params_lcs_openn_curr_specif_hyp4 %>% filter(term=="goals ~~ d_openn_1"),
params_lcs_openn_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_openn_1"),
#facets
params_lcs_socia_curr_hyp4 %>% filter(term=="d_socia_1 ~~ sb06_01_t1"),
params_lcs_socia_ideal_hyp4 %>% filter(term=="d_socia_1 ~~ sb06_01_t1"),
params_lcs_socia_curr_specif_hyp4 %>% filter(term=="d_socia_1 ~~ sb07_01_t1"),
params_lcs_socia_ideal_specif_hyp4 %>% filter(term=="d_socia_1 ~~ sb07_01_t1"),
params_lcs_asser_curr_hyp4 %>% filter(term=="d_asser_1 ~~ sb06_01_t1"),
params_lcs_asser_ideal_hyp4 %>% filter(term=="d_asser_1 ~~ sb06_01_t1"),
params_lcs_asser_curr_specif_hyp4 %>% filter(term=="d_asser_1 ~~ sb07_02_t1"),
params_lcs_asser_ideal_specif_hyp4 %>% filter(term=="d_asser_1 ~~ sb07_02_t1"),
params_lcs_energ_curr_hyp4 %>% filter(term=="d_energ_1 ~~ sb06_01_t1"),
params_lcs_energ_ideal_hyp4 %>% filter(term=="d_energ_1 ~~ sb06_01_t1"),
params_lcs_energ_curr_specif_hyp4 %>% filter(term=="d_energ_1 ~~ sb07_03_t1"),
params_lcs_energ_ideal_specif_hyp4 %>% filter(term=="d_energ_1 ~~ sb07_03_t1"),
params_lcs_compa_curr_hyp4 %>% filter(term=="d_compa_1 ~~ sb06_01_t1"),
params_lcs_compa_ideal_hyp4 %>% filter(term=="d_compa_1 ~~ sb06_01_t1"),
params_lcs_compa_curr_specif_hyp4 %>% filter(term=="d_compa_1 ~~ sb07_04_t1"),
params_lcs_compa_ideal_specif_hyp4 %>% filter(term=="d_compa_1 ~~ sb07_04_t1"),
params_lcs_respe_curr_hyp4 %>% filter(term=="d_respe_1 ~~ sb06_01_t1"),
params_lcs_respe_ideal_hyp4 %>% filter(term=="d_respe_1 ~~ sb06_01_t1"),
params_lcs_respe_curr_specif_hyp4 %>% filter(term=="d_respe_1 ~~ sb07_05_t1"),
params_lcs_respe_ideal_specif_hyp4 %>% filter(term=="d_respe_1 ~~ sb07_05_t1"),
params_lcs_trust_curr_hyp4 %>% filter(term=="d_trust_1 ~~ sb06_01_t1"),
params_lcs_trust_ideal_hyp4 %>% filter(term=="d_trust_1 ~~ sb06_01_t1"),
params_lcs_trust_curr_specif_hyp4 %>% filter(term=="d_trust_1 ~~ sb07_06_t1"),
params_lcs_trust_ideal_specif_hyp4 %>% filter(term=="d_trust_1 ~~ sb07_06_t1"),
params_lcs_organ_curr_hyp4 %>% filter(term=="d_organ_1 ~~ sb06_01_t1"),
params_lcs_organ_ideal_hyp4 %>% filter(term=="d_organ_1 ~~ sb06_01_t1"),
params_lcs_organ_curr_specif_hyp4 %>% filter(term=="d_organ_1 ~~ sb07_07_t1"),
params_lcs_organ_ideal_specif_hyp4 %>% filter(term=="d_organ_1 ~~ sb07_07_t1"),
params_lcs_produ_curr_hyp4 %>% filter(term=="d_produ_1 ~~ sb06_01_t1"),
params_lcs_produ_ideal_hyp4 %>% filter(term=="d_produ_1 ~~ sb06_01_t1"),
params_lcs_produ_curr_specif_hyp4 %>% filter(term=="d_produ_1 ~~ sb07_08_t1"),
params_lcs_produ_ideal_specif_hyp4 %>% filter(term=="d_produ_1 ~~ sb07_08_t1"),
params_lcs_respo_curr_hyp4 %>% filter(term=="d_respo_1 ~~ sb06_01_t1"),
params_lcs_respo_ideal_hyp4 %>% filter(term=="d_respo_1 ~~ sb06_01_t1"),
params_lcs_respo_curr_specif_hyp4 %>% filter(term=="d_respo_1 ~~ sb07_09_t1"),
params_lcs_respo_ideal_specif_hyp4 %>% filter(term=="d_respo_1 ~~ sb07_09_t1"),
params_lcs_anxie_curr_hyp4 %>% filter(term=="d_anxie_1 ~~ sb06_01_t1"),
params_lcs_anxie_ideal_hyp4 %>% filter(term=="d_anxie_1 ~~ sb06_01_t1"),
params_lcs_anxie_curr_specif_hyp4 %>% filter(term=="d_anxie_1 ~~ sb07_10_t1"),
params_lcs_anxie_ideal_specif_hyp4 %>% filter(term=="d_anxie_1 ~~ sb07_10_t1"),
params_lcs_depre_curr_hyp4 %>% filter(term=="d_depre_1 ~~ sb06_01_t1"),
params_lcs_depre_ideal_hyp4 %>% filter(term=="d_depre_1 ~~ sb06_01_t1"),
params_lcs_depre_curr_specif_hyp4 %>% filter(term=="d_depre_1 ~~ sb07_11_t1"),
params_lcs_depre_ideal_specif_hyp4 %>% filter(term=="d_depre_1 ~~ sb07_11_t1"),
params_lcs_volat_curr_hyp4 %>% filter(term=="d_volat_1 ~~ sb06_01_t1"),
params_lcs_volat_ideal_hyp4 %>% filter(term=="d_volat_1 ~~ sb06_01_t1"),
params_lcs_volat_curr_specif_hyp4 %>% filter(term=="d_volat_1 ~~ sb07_12_t1"),
params_lcs_volat_ideal_specif_hyp4 %>% filter(term=="d_volat_1 ~~ sb07_12_t1"),
params_lcs_curio_curr_hyp4 %>% filter(term=="d_curio_1 ~~ sb06_01_t1"),
params_lcs_curio_ideal_hyp4 %>% filter(term=="d_curio_1 ~~ sb06_01_t1"),
params_lcs_curio_curr_specif_hyp4 %>% filter(term=="d_curio_1 ~~ sb07_13_t1"),
params_lcs_curio_ideal_specif_hyp4 %>% filter(term=="d_curio_1 ~~ sb07_13_t1"),
params_lcs_aesth_curr_hyp4 %>% filter(term=="d_aesth_1 ~~ sb06_01_t1"),
params_lcs_aesth_ideal_hyp4 %>% filter(term=="d_aesth_1 ~~ sb06_01_t1"),
params_lcs_aesth_curr_specif_hyp4 %>% filter(term=="d_aesth_1 ~~ sb07_14_t1"),
params_lcs_aesth_ideal_specif_hyp4 %>% filter(term=="d_aesth_1 ~~ sb07_14_t1"),
params_lcs_imagi_curr_hyp4 %>% filter(term=="d_imagi_1 ~~ sb06_01_t1"),
params_lcs_imagi_ideal_hyp4 %>% filter(term=="d_imagi_1 ~~ sb06_01_t1"),
params_lcs_imagi_curr_specif_hyp4 %>% filter(term=="d_imagi_1 ~~ sb07_15_t1"),
params_lcs_imagi_ideal_specif_hyp4 %>% filter(term=="d_imagi_1 ~~ sb07_15_t1"),
) %>%
mutate(trait = rep(names(b5_vars), each=4),
ref = rep(rep(c("current", "ideal"), 2), 20),
goal = rep(c(rep("general", 2), rep("specific", 2)), 20)) %>%
select(trait, ref, goal, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp4_plot <- df_table_hyp4_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor(ref, levels = c("current", "ideal"), labels = c("current", "ideal"))) %>%
mutate(goal = factor(goal, levels = c("general", "specific"), labels = c("general", "specific"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_hyp4_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + goal, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
In the self-acceptance group, there will be a correlation between acceptance goals and change in ideal-self ratings but not change in current-self ratings.
We will test this one domain/facet at a time. We will use both general continuous change goal score as well as trait-specific change goals. To test this hypothesis, we will estimate the mean-level difference across time for both current and ideal trait ratings using latent change models and correlate change goals with the change variable from those models.
Reshape and split data set by intervention group:
df_sbsa_wide_pers_sa <- df_sbsa %>%
filter(rando=="Self-Acceptance") %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sa07")), # facet-specific change goals
sa06_01) %>% # general change goal
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with("sa07"), sa06_01)) %>%
select(-c(sa07_01_t2, sa07_02_t2, sa07_03_t2, sa07_04_t2, sa07_05_t2,
sa07_06_t2, sa07_07_t2, sa07_08_t2, sa07_09_t2, sa07_10_t2,
sa07_11_t2, sa07_12_t2, sa07_13_t2, sa07_14_t2, sa07_15_t2, sa06_01_t2))
# colnames(df_sbsa_wide_pers_sa)
group_assign <- df_sbsa %>% select(pid, rando) %>% unique()
df_sbsa_wide_pers_sa <- df_sbsa_wide_pers %>% left_join(group_assign) %>% filter(rando=="Self-Acceptance") %>% select(-rando) %>%
left_join(df_sbsa_wide_pers_sa)Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_curr_hyp5 <- '
extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
extra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1
extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1
extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1
extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2
extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2
extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2
extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_extra_curr_hyp5 <- lavaan(mi_lcs_extra_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 18 | 4089.128 | 4155.916 | 0.989 | 0.051 | 0.042 |
# parameters of interest
params_lcs_extra_curr_hyp5 <- broom::tidy(fit_mi_lcs_extra_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.034 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.563 | NA | NA |
| d_extra_1 ~1 | 0.650 | 0.404 | 0.895 | 1.737 | 5.190 | 0.000 |
| extra_t1 ~1 | 2.762 | 2.674 | 2.850 | 4.023 | 61.719 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.125 | 0.074 | 0.175 | 0.890 | 4.821 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.181 | -0.267 | -0.094 | -0.332 | -4.096 | 0.000 |
| d_extra_1 ~~ sa06_01_t1 | 0.047 | 0.006 | 0.088 | 0.140 | 2.273 | 0.023 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.191 | 4.082 | 4.299 | 4.402 | 75.693 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_ideal_hyp5 <- '
extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
extra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_extra_ideal_hyp5 <- lavaan(mi_lcs_extra_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 18 | 3504.32 | 3571.108 | 0.992 | 0.029 | 0.039 |
# parameters of interest
params_lcs_extra_ideal_hyp5 <- broom::tidy(fit_mi_lcs_extra_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.052 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.790 | NA | NA |
| d_extra_1 ~1 | 1.237 | 0.707 | 1.767 | 4.674 | 4.573 | 0.000 |
| extra_t1 ~1 | 3.843 | 3.777 | 3.909 | 10.901 | 113.832 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.057 | 0.027 | 0.086 | 0.807 | 3.724 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.330 | -0.467 | -0.192 | -0.439 | -4.709 | 0.000 |
| d_extra_1 ~~ sa06_01_t1 | 0.018 | -0.013 | 0.050 | 0.081 | 1.133 | 0.257 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.082 | 4.299 | 4.402 | 75.694 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_curr_specif_hyp5 <- '
extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
goals =~ 1*sa07_01_t1 + sa07_02_t1 + sa07_03_t1 # latent acceptance goal variable (three facets per trait)
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
extra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1
extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1
extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1
extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2
extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2
extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2
extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_01_t1 ~~ sa07_01_t1
sa07_02_t1 ~~ sa07_02_t1
sa07_03_t1 ~~ sa07_03_t1
sa07_01_t1 ~ 1
sa07_02_t1 ~ 1
sa07_03_t1 ~ 1
'
fit_mi_lcs_extra_curr_specif_hyp5 <- lavaan(mi_lcs_extra_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 25 | 6292.257 | 6385.017 | 0.899 | 0.127 | 0.194 |
# parameters of interest
params_lcs_extra_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_extra_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"goals ~~ d_extra_1", "goals ~~ goals", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.024 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.552 | NA | NA |
| d_extra_1 ~1 | 0.616 | 0.343 | 0.890 | 1.665 | 4.419 | 0.000 |
| extra_t1 ~1 | 2.762 | 2.675 | 2.850 | 4.027 | 61.752 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.124 | 0.073 | 0.174 | 0.902 | 4.825 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.169 | -0.267 | -0.070 | -0.312 | -3.368 | 0.001 |
| goals ~~ d_extra_1 | 0.024 | -0.040 | 0.089 | 0.082 | 0.739 | 0.460 |
| goals ~~ goals | 0.717 | 0.329 | 1.105 | 1.000 | 3.621 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_ideal_specif_hyp5 <- '
extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
goals =~ 1*sa07_01_t1 + sa07_02_t1 + sa07_03_t1 # latent acceptance goal variable (three facets per trait)
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
extra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_01_t1 ~~ sa07_01_t1
sa07_02_t1 ~~ sa07_02_t1
sa07_03_t1 ~~ sa07_03_t1
sa07_01_t1 ~ 1
sa07_02_t1 ~ 1
sa07_03_t1 ~ 1
'
fit_mi_lcs_extra_ideal_specif_hyp5 <- lavaan(mi_lcs_extra_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 25 | 5702.262 | 5795.023 | 0.955 | 0.057 | 0.049 |
# parameters of interest
params_lcs_extra_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_extra_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"goals ~~ d_extra_1", "goals ~~ goals", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.048 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.784 | NA | NA |
| d_extra_1 ~1 | 1.217 | 0.691 | 1.743 | 4.679 | 4.538 | 0.000 |
| extra_t1 ~1 | 3.843 | 3.777 | 3.909 | 11.053 | 114.075 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.055 | 0.026 | 0.084 | 0.812 | 3.679 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.324 | -0.460 | -0.189 | -0.434 | -4.677 | 0.000 |
| goals ~~ d_extra_1 | -0.035 | -0.079 | 0.009 | -0.179 | -1.543 | 0.123 |
| goals ~~ goals | 0.696 | 0.319 | 1.073 | 1.000 | 3.617 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_curr_hyp5 <- '
agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
agree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1
agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1
agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1
agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2
agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2
agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2
agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_agree_curr_hyp5 <- lavaan(mi_lcs_agree_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 18 | 3547.09 | 3613.878 | 0.979 | 0.055 | 0.065 |
# parameters of interest
params_lcs_agree_curr_hyp5 <- broom::tidy(fit_mi_lcs_agree_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.039 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.565 | NA | NA |
| d_agree_1 ~1 | 0.644 | -0.018 | 1.307 | 3.041 | 1.905 | 0.057 |
| agree_t1 ~1 | 3.336 | 3.268 | 3.403 | 8.557 | 96.584 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.040 | 0.007 | 0.072 | 0.885 | 2.406 | 0.016 |
| d_agree_1 ~ agree_t1 | -0.184 | -0.379 | 0.010 | -0.339 | -1.858 | 0.063 |
| d_agree_1 ~~ sa06_01_t1 | 0.029 | -0.008 | 0.065 | 0.150 | 1.548 | 0.122 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.611 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_ideal_hyp5 <- '
agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
agree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_agree_ideal_hyp5 <- lavaan(mi_lcs_agree_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 18 | 3711.06 | 3777.848 | 0.986 | 0.044 | 0.069 |
# parameters of interest
params_lcs_agree_ideal_hyp5 <- broom::tidy(fit_mi_lcs_agree_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 0.954 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.617 | NA | NA |
| d_agree_1 ~1 | 0.598 | 0.160 | 1.035 | 2.023 | 2.679 | 0.007 |
| agree_t1 ~1 | 3.936 | 3.861 | 4.011 | 8.618 | 103.274 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.082 | 0.044 | 0.119 | 0.939 | 4.281 | 0.000 |
| d_agree_1 ~ agree_t1 | -0.160 | -0.268 | -0.052 | -0.248 | -2.905 | 0.004 |
| d_agree_1 ~~ sa06_01_t1 | 0.001 | -0.040 | 0.042 | 0.003 | 0.037 | 0.970 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.632 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_curr_specif_hyp5 <- '
agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
goals =~ 1*sa07_04_t1 + sa07_05_t1 + sa07_06_t1 # latent acceptance goal variable (three facets per trait)
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
agree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1
agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1
agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1
agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2
agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2
agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2
agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_04_t1 ~~ sa07_04_t1
sa07_05_t1 ~~ sa07_05_t1
sa07_06_t1 ~~ sa07_06_t1
sa07_04_t1 ~ 1
sa07_05_t1 ~ 1
sa07_06_t1 ~ 1
'
fit_mi_lcs_agree_curr_specif_hyp5 <- lavaan(mi_lcs_agree_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 25 | 5605.423 | 5698.184 | 0.966 | 0.06 | 0.083 |
# parameters of interest
params_lcs_agree_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_agree_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"goals ~~ d_agree_1", "goals ~~ goals", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.021 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.544 | NA | NA |
| d_agree_1 ~1 | 0.569 | -0.052 | 1.190 | 2.729 | 1.796 | 0.072 |
| agree_t1 ~1 | 3.336 | 3.268 | 3.403 | 8.519 | 96.513 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.039 | 0.007 | 0.072 | 0.908 | 2.377 | 0.017 |
| d_agree_1 ~ agree_t1 | -0.162 | -0.344 | 0.020 | -0.304 | -1.740 | 0.082 |
| goals ~~ d_agree_1 | 0.002 | -0.031 | 0.034 | 0.011 | 0.105 | 0.916 |
| goals ~~ goals | 0.624 | 0.338 | 0.910 | 1.000 | 4.274 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_ideal_specif_hyp5 <- '
agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
goals =~ 1*sa07_04_t1 + sa07_05_t1 + sa07_06_t1 # latent acceptance goal variable (three facets per trait)
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
agree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_04_t1 ~~ sa07_04_t1
sa07_05_t1 ~~ sa07_05_t1
sa07_06_t1 ~~ sa07_06_t1
sa07_04_t1 ~ 1
sa07_05_t1 ~ 1
sa07_06_t1 ~ 1
'
fit_mi_lcs_agree_ideal_specif_hyp5 <- lavaan(mi_lcs_agree_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 25 | 5765.942 | 5858.703 | 1 | 0 | 0.03 |
# parameters of interest
params_lcs_agree_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_agree_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"goals ~~ d_agree_1", "goals ~~ goals", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 0.954 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.617 | NA | NA |
| d_agree_1 ~1 | 0.596 | 0.181 | 1.011 | 2.020 | 2.814 | 0.005 |
| agree_t1 ~1 | 3.936 | 3.861 | 4.011 | 8.625 | 103.268 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.082 | 0.044 | 0.119 | 0.939 | 4.293 | 0.000 |
| d_agree_1 ~ agree_t1 | -0.160 | -0.262 | -0.057 | -0.247 | -3.051 | 0.002 |
| goals ~~ d_agree_1 | -0.013 | -0.059 | 0.033 | -0.057 | -0.548 | 0.583 |
| goals ~~ goals | 0.633 | 0.342 | 0.924 | 1.000 | 4.265 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_curr_hyp5 <- '
consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
consc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1
consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1
consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1
consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2
consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2
consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2
consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_consc_curr_hyp5 <- lavaan(mi_lcs_consc_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 18 | 3922.223 | 3989.01 | 1 | 0 | 0.022 |
# parameters of interest
params_lcs_consc_curr_hyp5 <- broom::tidy(fit_mi_lcs_consc_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.031 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.448 | NA | NA |
| d_consc_1 ~1 | 0.535 | 0.282 | 0.787 | 1.595 | 4.151 | 0.000 |
| consc_t1 ~1 | 3.195 | 3.097 | 3.292 | 4.139 | 64.256 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.103 | 0.059 | 0.148 | 0.918 | 4.532 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.124 | -0.198 | -0.050 | -0.286 | -3.271 | 0.001 |
| d_consc_1 ~~ sa06_01_t1 | -0.003 | -0.043 | 0.038 | -0.008 | -0.126 | 0.899 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.299 | 4.401 | 75.439 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_ideal_hyp5 <- '
consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
consc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_consc_ideal_hyp5 <- lavaan(mi_lcs_consc_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 18 | 3097.49 | 3164.277 | 0.982 | 0.05 | 0.046 |
# parameters of interest
params_lcs_consc_ideal_hyp5 <- broom::tidy(fit_mi_lcs_consc_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.976 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.740 | NA | NA |
| d_consc_1 ~1 | 1.171 | 0.427 | 1.916 | 3.799 | 3.083 | 0.002 |
| consc_t1 ~1 | 4.392 | 4.335 | 4.449 | 10.795 | 151.675 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.084 | 0.053 | 0.114 | 0.880 | 5.387 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.262 | -0.427 | -0.098 | -0.346 | -3.121 | 0.002 |
| d_consc_1 ~~ sa06_01_t1 | -0.022 | -0.058 | 0.014 | -0.080 | -1.189 | 0.235 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.602 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.568 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_curr_specif_hyp5 <- '
consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
goals =~ 1*sa07_07_t1 + sa07_08_t1 + sa07_09_t1 # latent acceptance goal variable (three facets per trait)
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
consc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1
consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1
consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1
consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2
consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2
consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2
consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_07_t1 ~~ sa07_07_t1
sa07_08_t1 ~~ sa07_08_t1
sa07_09_t1 ~~ sa07_09_t1
sa07_07_t1 ~ 1
sa07_08_t1 ~ 1
sa07_09_t1 ~ 1
'
fit_mi_lcs_consc_curr_specif_hyp5 <- lavaan(mi_lcs_consc_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 25 | 6024.823 | 6117.583 | 0.919 | 0.137 | 0.254 |
# parameters of interest
params_lcs_consc_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_consc_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"goals ~~ d_consc_1", "goals ~~ goals", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.101 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.522 | NA | NA |
| d_consc_1 ~1 | 0.777 | 0.454 | 1.101 | 2.121 | 4.708 | 0.000 |
| consc_t1 ~1 | 3.195 | 3.097 | 3.292 | 4.135 | 64.279 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.110 | 0.062 | 0.158 | 0.822 | 4.507 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.200 | -0.298 | -0.102 | -0.421 | -4.016 | 0.000 |
| goals ~~ d_consc_1 | -0.115 | -0.201 | -0.029 | -0.300 | -2.610 | 0.009 |
| goals ~~ goals | 1.328 | 1.002 | 1.653 | 1.000 | 7.998 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_ideal_specif_hyp5 <- '
consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
goals =~ 1*sa07_07_t1 + sa07_08_t1 + sa07_09_t1 # latent acceptance goal variable (three facets per trait)
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
consc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_07_t1 ~~ sa07_07_t1
sa07_08_t1 ~~ sa07_08_t1
sa07_09_t1 ~~ sa07_09_t1
sa07_07_t1 ~ 1
sa07_08_t1 ~ 1
sa07_09_t1 ~ 1
'
fit_mi_lcs_consc_ideal_specif_hyp5 <- lavaan(mi_lcs_consc_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 25 | 5207.504 | 5300.265 | 0.984 | 0.043 | 0.046 |
# parameters of interest
params_lcs_consc_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_consc_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"goals ~~ d_consc_1", "goals ~~ goals", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.985 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.754 | NA | NA |
| d_consc_1 ~1 | 1.237 | 0.511 | 1.963 | 3.953 | 3.339 | 0.001 |
| consc_t1 ~1 | 4.392 | 4.335 | 4.449 | 10.743 | 151.518 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.085 | 0.054 | 0.116 | 0.869 | 5.382 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.277 | -0.438 | -0.117 | -0.362 | -3.384 | 0.001 |
| goals ~~ d_consc_1 | -0.040 | -0.093 | 0.013 | -0.120 | -1.485 | 0.137 |
| goals ~~ goals | 1.315 | 0.983 | 1.648 | 1.000 | 7.764 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_curr_hyp5 <- '
neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
neuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_neuro_curr_hyp5 <- lavaan(mi_lcs_neuro_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 18 | 4357.621 | 4424.408 | 0.983 | 0.07 | 0.077 |
# parameters of interest
params_lcs_neuro_curr_hyp5 <- broom::tidy(fit_mi_lcs_neuro_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.990 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.538 | NA | NA |
| d_neuro_1 ~1 | 0.308 | -0.019 | 0.634 | 0.712 | 1.845 | 0.065 |
| neuro_t1 ~1 | 3.521 | 3.420 | 3.621 | 4.430 | 68.676 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.175 | 0.118 | 0.231 | 0.936 | 6.051 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.137 | -0.228 | -0.047 | -0.252 | -2.978 | 0.003 |
| d_neuro_1 ~~ sa06_01_t1 | -0.052 | -0.111 | 0.007 | -0.131 | -1.725 | 0.085 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.907 | 0.722 | 1.092 | 1.000 | 9.590 | 0.000 |
| sa06_01_t1 ~1 | 4.192 | 4.083 | 4.300 | 4.401 | 75.809 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_ideal_hyp5 <- '
neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_neuro_ideal_hyp5 <- lavaan(mi_lcs_neuro_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 18 | 3389.72 | 3456.507 | 0.992 | 0.03 | 0.039 |
# parameters of interest
params_lcs_neuro_ideal_hyp5 <- broom::tidy(fit_mi_lcs_neuro_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.900 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.775 | NA | NA |
| d_neuro_1 ~1 | 0.434 | 0.154 | 0.714 | 1.343 | 3.037 | 0.002 |
| neuro_t1 ~1 | 1.658 | 1.602 | 1.714 | 4.416 | 58.314 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.095 | 0.057 | 0.134 | 0.913 | 4.818 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.253 | -0.429 | -0.078 | -0.294 | -2.829 | 0.005 |
| d_neuro_1 ~~ sa06_01_t1 | -0.017 | -0.060 | 0.025 | -0.060 | -0.802 | 0.422 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.555 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_curr_specif_hyp5 <- '
neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
goals =~ 1*sa07_10_t1 + sa07_11_t1 + sa07_12_t1 # latent acceptance goal variable (three facets per trait)
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
neuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_10_t1 ~~ sa07_10_t1
sa07_11_t1 ~~ sa07_11_t1
sa07_12_t1 ~~ sa07_12_t1
sa07_10_t1 ~ 1
sa07_11_t1 ~ 1
sa07_12_t1 ~ 1
'
fit_mi_lcs_neuro_curr_specif_hyp5 <- lavaan(mi_lcs_neuro_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 25 | 6501.688 | 6594.449 | 0.948 | 0.1 | 0.153 |
# parameters of interest
params_lcs_neuro_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_neuro_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"goals ~~ d_neuro_1", "goals ~~ goals", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.020 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.569 | NA | NA |
| d_neuro_1 ~1 | 0.441 | 0.083 | 0.799 | 0.993 | 2.415 | 0.016 |
| neuro_t1 ~1 | 3.521 | 3.420 | 3.621 | 4.422 | 68.663 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.178 | 0.120 | 0.235 | 0.901 | 6.045 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.175 | -0.276 | -0.074 | -0.314 | -3.405 | 0.001 |
| goals ~~ d_neuro_1 | 0.035 | -0.031 | 0.101 | 0.097 | 1.042 | 0.297 |
| goals ~~ goals | 0.743 | 0.349 | 1.138 | 1.000 | 3.695 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_ideal_specif_hyp5 <- '
neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
goals =~ 1*sa07_10_t1 + sa07_11_t1 + sa07_12_t1 # latent acceptance goal variable (three facets per trait)
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_10_t1 ~~ sa07_10_t1
sa07_11_t1 ~~ sa07_11_t1
sa07_12_t1 ~~ sa07_12_t1
sa07_10_t1 ~ 1
sa07_11_t1 ~ 1
sa07_12_t1 ~ 1
'
fit_mi_lcs_neuro_ideal_specif_hyp5 <- lavaan(mi_lcs_neuro_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 25 | 5527.485 | 5620.245 | 0.965 | 0.054 | 0.049 |
# parameters of interest
params_lcs_neuro_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_neuro_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"goals ~~ d_neuro_1", "goals ~~ goals", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.896 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.769 | NA | NA |
| d_neuro_1 ~1 | 0.423 | 0.149 | 0.696 | 1.297 | 3.025 | 0.002 |
| neuro_t1 ~1 | 1.658 | 1.602 | 1.713 | 4.370 | 58.300 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.097 | 0.057 | 0.138 | 0.918 | 4.750 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.246 | -0.417 | -0.074 | -0.286 | -2.811 | 0.005 |
| goals ~~ d_neuro_1 | 0.045 | 0.011 | 0.079 | 0.183 | 2.622 | 0.009 |
| goals ~~ goals | 0.631 | 0.147 | 1.115 | 1.000 | 2.553 | 0.011 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_curr_hyp5 <- '
openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
openn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1
openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1
openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1
openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2
openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2
openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2
openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_openn_curr_hyp5 <- lavaan(mi_lcs_openn_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 18 | 3748.529 | 3815.317 | 1 | 0 | 0.036 |
# parameters of interest
params_lcs_openn_curr_hyp5 <- broom::tidy(fit_mi_lcs_openn_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.037 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.557 | NA | NA |
| d_openn_1 ~1 | 0.702 | 0.337 | 1.066 | 2.408 | 3.768 | 0.000 |
| openn_t1 ~1 | 3.578 | 3.502 | 3.654 | 6.602 | 92.688 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.075 | 0.047 | 0.104 | 0.889 | 5.121 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.179 | -0.278 | -0.080 | -0.333 | -3.558 | 0.000 |
| d_openn_1 ~~ sa06_01_t1 | 0.003 | -0.035 | 0.042 | 0.013 | 0.174 | 0.862 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.598 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_ideal_hyp5 <- '
openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
openn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_openn_ideal_hyp5 <- lavaan(mi_lcs_openn_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='WLSMV', fixed.x=FALSE, ordered="sa06_01_t1")
# This model did not converge properly (when adding the 'sa06_01_t1' goal variable). Declaring 'sa06_01_t1' as an
# ordered variable and using the WLSMV estimator (sadly without FIML) worked in the end. Results
# https://lavaan.ugent.be/tutorial/cat.html
summary(fit_mi_lcs_openn_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 18 | NA | NA | 0.996 | 0.022 | 0.053 |
# parameters of interest
params_lcs_openn_ideal_hyp5 <- broom::tidy(fit_mi_lcs_openn_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.953 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.675 | NA | NA |
| d_openn_1 ~1 | 0.748 | 0.271 | 1.225 | 2.690 | 3.075 | 0.002 |
| openn_t1 ~1 | 3.896 | 3.827 | 3.966 | 9.921 | 109.850 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.071 | 0.042 | 0.100 | 0.920 | 4.777 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.200 | -0.325 | -0.075 | -0.283 | -3.146 | 0.002 |
| d_openn_1 ~~ sa06_01_t1 | 0.050 | -0.004 | 0.104 | 0.187 | 1.799 | 0.072 |
| sa06_01_t1 ~~ sa06_01_t1 | 1.000 | -0.960 | 2.960 | 1.000 | 1.000 | 0.317 |
| sa06_01_t1 ~1 | 0.000 | -0.132 | 0.132 | 0.000 | 0.000 | 1.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_curr_specif_hyp5 <- '
openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
goals =~ 1*sa07_13_t1 + sa07_14_t1 + sa07_15_t1 # latent acceptance goal variable (three facets per trait)
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
openn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1
openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1
openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1
openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2
openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2
openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2
openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_13_t1 ~~ sa07_13_t1
sa07_14_t1 ~~ sa07_14_t1
sa07_15_t1 ~~ sa07_15_t1
sa07_13_t1 ~ 1
sa07_14_t1 ~ 1
sa07_15_t1 ~ 1
'
fit_mi_lcs_openn_curr_specif_hyp5 <- lavaan(mi_lcs_openn_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 25 | 5716.504 | 5809.264 | 0.962 | 0.078 | 0.152 |
# parameters of interest
params_lcs_openn_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_openn_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"goals ~~ d_openn_1", "goals ~~ goals", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.061 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.587 | NA | NA |
| d_openn_1 ~1 | 0.808 | 0.431 | 1.185 | 2.702 | 4.201 | 0.000 |
| openn_t1 ~1 | 3.578 | 3.503 | 3.654 | 6.623 | 92.709 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.077 | 0.048 | 0.106 | 0.857 | 5.175 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.209 | -0.311 | -0.107 | -0.378 | -4.000 | 0.000 |
| goals ~~ d_openn_1 | -0.030 | -0.065 | 0.005 | -0.151 | -1.683 | 0.092 |
| goals ~~ goals | 0.514 | 0.285 | 0.743 | 1.000 | 4.399 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_ideal_specif_hyp5 <- '
openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
goals =~ 1*sa07_13_t1 + sa07_14_t1 + sa07_15_t1 # latent acceptance goal variable (three facets per trait)
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
openn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_13_t1 ~~ sa07_13_t1
sa07_14_t1 ~~ sa07_14_t1
sa07_15_t1 ~~ sa07_15_t1
sa07_13_t1 ~ 1
sa07_14_t1 ~ 1
sa07_15_t1 ~ 1
'
fit_mi_lcs_openn_ideal_specif_hyp5 <- lavaan(mi_lcs_openn_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='WLSMV', fixed.x=FALSE, ordered=c("sa07_13_t1", "sa07_14_t1", "sa07_15_t1"))
# This model did not converge properly (when adding the 'sa06_01_t1' goal variable).
# Declaring c("sa07_13_t1", "sa07_14_t1", "sa07_15_t1") as
# ordered variables and using the WLSMV estimator (sadly without FIML) worked in the end. Results
# https://lavaan.ugent.be/tutorial/cat.html
summary(fit_mi_lcs_openn_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 25 | NA | NA | 0.999 | 0.011 | 0.052 |
# parameters of interest
params_lcs_openn_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_openn_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"goals ~~ d_openn_1", "goals ~~ goals", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.954 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.675 | NA | NA |
| d_openn_1 ~1 | 0.750 | 0.274 | 1.227 | 2.703 | 3.085 | 0.002 |
| openn_t1 ~1 | 3.896 | 3.827 | 3.966 | 9.938 | 109.881 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.071 | 0.042 | 0.100 | 0.920 | 4.789 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.201 | -0.326 | -0.076 | -0.284 | -3.156 | 0.002 |
| goals ~~ d_openn_1 | -0.036 | -0.070 | -0.002 | -0.220 | -2.098 | 0.036 |
| goals ~~ goals | 0.384 | 0.284 | 0.483 | 1.000 | 7.548 | 0.000 |
Run models for all facets with a template & loop:
# create template:
facet_template <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
d_facet_1 ~ facet_t1 # This estimates the self-feedback parameter
d_facet_1 ~~ ind_goal # estimates the covariance/correlation with acceptance goal variable
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# loop across 2 BFI versions (combined pre&post current/ideal)
for (j in 5:length(bfi_versions)) {
items = paste0(bfi_versions[[j]], item_nrs)
# loop across 2 different goal operationalizations (sa06_01_t1 & sa07_XX_t1)
for (k in 1:2) {
if (k==1) {
goal_op = "sa06_01_t1"
} else{
goal_op = paste0("sa07_", str_pad(i-5, 2, pad = "0"), "_t1")
}
template_filled <- str_replace_all(facet_template,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = goal_op))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
# save to environment
if (k==1) {
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_hyp5")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_hyp5")), facet_model_fit))
} else{
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_specif_hyp5")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_specif_hyp5")), facet_model_fit))
}
}
}
} Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7231.696 | 7313.325 | 0.971 | 0.064 | 0.049 |
# parameters of interest
params_lcs_socia_curr_hyp5 <- broom::tidy(fit_mi_lcs_socia_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.042 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.450 | NA | NA |
| d_socia_1 ~1 | 0.536 | 0.320 | 0.753 | 1.395 | 4.853 | 0.000 |
| socia_t1 ~1 | 2.799 | 2.658 | 2.941 | 3.143 | 38.832 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.134 | 0.063 | 0.204 | 0.905 | 3.718 | 0.000 |
| d_socia_1 ~ socia_t1 | -0.133 | -0.210 | -0.056 | -0.308 | -3.388 | 0.001 |
| d_socia_1 ~~ sa06_01_t1 | 0.062 | 0.008 | 0.115 | 0.177 | 2.239 | 0.025 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.188 | 4.079 | 4.297 | 4.399 | 75.378 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7191.725 | 7273.355 | 0.947 | 0.052 | 0.054 |
# parameters of interest
params_lcs_socia_ideal_hyp5 <- broom::tidy(fit_mi_lcs_socia_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.001 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.701 | NA | NA |
| d_socia_1 ~1 | 1.019 | 0.203 | 1.835 | 3.952 | 2.447 | 0.014 |
| socia_t1 ~1 | 4.317 | 4.241 | 4.394 | 11.726 | 110.171 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.058 | 0.008 | 0.108 | 0.877 | 2.289 | 0.022 |
| d_socia_1 ~ socia_t1 | -0.246 | -0.432 | -0.060 | -0.351 | -2.592 | 0.010 |
| d_socia_1 ~~ sa06_01_t1 | -0.005 | -0.042 | 0.033 | -0.020 | -0.239 | 0.811 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.589 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7448.839 | 7530.468 | 0.918 | 0.11 | 0.144 |
# parameters of interest
params_lcs_socia_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_socia_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sa07_01_t1", "sa07_01_t1 ~~ sa07_01_t1", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.053 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.461 | NA | NA |
| d_socia_1 ~1 | 0.569 | 0.329 | 0.810 | 1.466 | 4.643 | 0.000 |
| socia_t1 ~1 | 2.800 | 2.659 | 2.941 | 3.154 | 38.868 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.134 | 0.065 | 0.204 | 0.890 | 3.795 | 0.000 |
| d_socia_1 ~ socia_t1 | -0.145 | -0.231 | -0.059 | -0.332 | -3.300 | 0.001 |
| d_socia_1 ~~ sa07_01_t1 | -0.012 | -0.103 | 0.079 | -0.024 | -0.257 | 0.797 |
| sa07_01_t1 ~~ sa07_01_t1 | 1.866 | 1.657 | 2.075 | 1.000 | 17.474 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7403.017 | 7484.647 | 0.923 | 0.063 | 0.06 |
# parameters of interest
params_lcs_socia_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_socia_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sa07_01_t1", "sa07_01_t1 ~~ sa07_01_t1", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.991 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.679 | NA | NA |
| d_socia_1 ~1 | 0.929 | 0.091 | 1.768 | 3.549 | 2.172 | 0.030 |
| socia_t1 ~1 | 4.318 | 4.241 | 4.395 | 11.292 | 109.553 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.061 | 0.007 | 0.115 | 0.892 | 2.221 | 0.026 |
| d_socia_1 ~ socia_t1 | -0.225 | -0.417 | -0.034 | -0.329 | -2.310 | 0.021 |
| d_socia_1 ~~ sa07_01_t1 | -0.045 | -0.134 | 0.044 | -0.133 | -0.986 | 0.324 |
| sa07_01_t1 ~~ sa07_01_t1 | 1.866 | 1.657 | 2.075 | 1.000 | 17.474 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7567.584 | 7649.213 | 0.983 | 0.04 | 0.044 |
# parameters of interest
params_lcs_asser_curr_hyp5 <- broom::tidy(fit_mi_lcs_asser_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.982 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.535 | NA | NA |
| d_asser_1 ~1 | 0.494 | 0.198 | 0.791 | 1.483 | 3.266 | 0.001 |
| asser_t1 ~1 | 3.103 | 2.985 | 3.220 | 5.068 | 51.703 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.105 | 0.050 | 0.159 | 0.943 | 3.775 | 0.000 |
| d_asser_1 ~ asser_t1 | -0.130 | -0.222 | -0.038 | -0.239 | -2.764 | 0.006 |
| d_asser_1 ~~ sa06_01_t1 | 0.044 | -0.006 | 0.094 | 0.144 | 1.738 | 0.082 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.907 | 0.722 | 1.092 | 1.000 | 9.596 | 0.000 |
| sa06_01_t1 ~1 | 4.192 | 4.084 | 4.301 | 4.403 | 75.795 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7403.665 | 7485.294 | 0.98 | 0.03 | 0.043 |
# parameters of interest
params_lcs_asser_ideal_hyp5 <- broom::tidy(fit_mi_lcs_asser_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.016 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.726 | NA | NA |
| d_asser_1 ~1 | 1.096 | 0.332 | 1.861 | 5.567 | 2.812 | 0.005 |
| asser_t1 ~1 | 4.148 | 4.052 | 4.244 | 15.047 | 84.520 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.033 | 0.003 | 0.064 | 0.856 | 2.143 | 0.032 |
| d_asser_1 ~ asser_t1 | -0.271 | -0.453 | -0.089 | -0.379 | -2.917 | 0.004 |
| d_asser_1 ~~ sa06_01_t1 | 0.017 | -0.015 | 0.049 | 0.100 | 1.059 | 0.290 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.602 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.082 | 4.299 | 4.401 | 75.670 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7785.074 | 7866.704 | 0.931 | 0.083 | 0.1 |
# parameters of interest
params_lcs_asser_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_asser_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sa07_02_t1", "sa07_02_t1 ~~ sa07_02_t1", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.988 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.541 | NA | NA |
| d_asser_1 ~1 | 0.519 | 0.201 | 0.837 | 1.540 | 3.196 | 0.001 |
| asser_t1 ~1 | 3.102 | 2.984 | 3.220 | 5.036 | 51.680 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.106 | 0.051 | 0.162 | 0.937 | 3.773 | 0.000 |
| d_asser_1 ~ asser_t1 | -0.138 | -0.237 | -0.038 | -0.252 | -2.706 | 0.007 |
| d_asser_1 ~~ sa07_02_t1 | -0.004 | -0.084 | 0.076 | -0.010 | -0.106 | 0.916 |
| sa07_02_t1 ~~ sa07_02_t1 | 1.876 | 1.675 | 2.076 | 1.000 | 18.353 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7618.606 | 7700.235 | 0.97 | 0.037 | 0.049 |
# parameters of interest
params_lcs_asser_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_asser_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sa07_02_t1", "sa07_02_t1 ~~ sa07_02_t1", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.009 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.715 | NA | NA |
| d_asser_1 ~1 | 1.051 | 0.280 | 1.822 | 5.347 | 2.673 | 0.008 |
| asser_t1 ~1 | 4.148 | 4.052 | 4.244 | 14.943 | 84.560 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.033 | 0.003 | 0.064 | 0.865 | 2.158 | 0.031 |
| d_asser_1 ~ asser_t1 | -0.260 | -0.443 | -0.076 | -0.367 | -2.776 | 0.006 |
| d_asser_1 ~~ sa07_02_t1 | -0.021 | -0.068 | 0.027 | -0.082 | -0.856 | 0.392 |
| sa07_02_t1 ~~ sa07_02_t1 | 1.876 | 1.675 | 2.076 | 1.000 | 18.354 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7685.109 | 7766.739 | 0.965 | 0.056 | 0.052 |
# parameters of interest
params_lcs_energ_curr_hyp5 <- broom::tidy(fit_mi_lcs_energ_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.048 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.645 | NA | NA |
| d_energ_1 ~1 | 0.542 | 0.282 | 0.803 | 1.882 | 4.081 | 0.000 |
| energ_t1 ~1 | 2.677 | 2.565 | 2.788 | 5.715 | 46.973 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.071 | 0.021 | 0.122 | 0.856 | 2.755 | 0.006 |
| d_energ_1 ~ energ_t1 | -0.234 | -0.333 | -0.134 | -0.380 | -4.600 | 0.000 |
| d_energ_1 ~~ sa06_01_t1 | -0.013 | -0.049 | 0.024 | -0.050 | -0.684 | 0.494 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.299 | 4.401 | 75.585 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6726.831 | 6808.461 | 0.964 | 0.033 | 0.049 |
# parameters of interest
params_lcs_energ_ideal_hyp5 <- broom::tidy(fit_mi_lcs_energ_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.094 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.753 | NA | NA |
| d_energ_1 ~1 | 0.765 | 0.316 | 1.214 | 6.889 | 3.338 | 0.001 |
| energ_t1 ~1 | 2.395 | 2.274 | 2.517 | 14.860 | 38.650 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.010 | -0.014 | 0.034 | 0.785 | 0.790 | 0.430 |
| d_energ_1 ~ energ_t1 | -0.319 | -0.508 | -0.131 | -0.463 | -3.317 | 0.001 |
| d_energ_1 ~~ sa06_01_t1 | -0.014 | -0.039 | 0.012 | -0.144 | -1.038 | 0.299 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.191 | 4.082 | 4.299 | 4.402 | 75.676 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7909.358 | 7990.988 | 0.882 | 0.108 | 0.13 |
# parameters of interest
params_lcs_energ_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_energ_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sa07_03_t1", "sa07_03_t1 ~~ sa07_03_t1", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.081 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.690 | NA | NA |
| d_energ_1 ~1 | 0.656 | 0.355 | 0.958 | 2.199 | 4.264 | 0.000 |
| energ_t1 ~1 | 2.677 | 2.565 | 2.788 | 5.726 | 47.016 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.072 | 0.022 | 0.123 | 0.813 | 2.813 | 0.005 |
| d_energ_1 ~ energ_t1 | -0.276 | -0.391 | -0.161 | -0.432 | -4.709 | 0.000 |
| d_energ_1 ~~ sa07_03_t1 | 0.047 | -0.022 | 0.116 | 0.125 | 1.344 | 0.179 |
| sa07_03_t1 ~~ sa07_03_t1 | 1.949 | 1.744 | 2.154 | 1.000 | 18.642 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6953.928 | 7035.558 | 0.95 | 0.039 | 0.051 |
# parameters of interest
params_lcs_energ_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_energ_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sa07_03_t1", "sa07_03_t1 ~~ sa07_03_t1", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.086 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.741 | NA | NA |
| d_energ_1 ~1 | 0.740 | 0.282 | 1.197 | 6.818 | 3.168 | 0.002 |
| energ_t1 ~1 | 2.395 | 2.274 | 2.516 | 15.055 | 38.653 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.009 | -0.014 | 0.033 | 0.795 | 0.779 | 0.436 |
| d_energ_1 ~ energ_t1 | -0.309 | -0.501 | -0.116 | -0.453 | -3.147 | 0.002 |
| d_energ_1 ~~ sa07_03_t1 | 0.013 | -0.021 | 0.047 | 0.096 | 0.752 | 0.452 |
| sa07_03_t1 ~~ sa07_03_t1 | 1.949 | 1.744 | 2.154 | 1.000 | 18.642 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7538.208 | 7619.837 | 0.932 | 0.067 | 0.068 |
# parameters of interest
params_lcs_compa_curr_hyp5 <- broom::tidy(fit_mi_lcs_compa_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.128 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.734 | NA | NA |
| d_compa_1 ~1 | 1.382 | 0.639 | 2.124 | 2.795 | 3.646 | 0.000 |
| compa_t1 ~1 | 4.135 | 4.025 | 4.244 | 5.441 | 74.196 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.186 | 0.065 | 0.306 | 0.760 | 3.017 | 0.003 |
| d_compa_1 ~ compa_t1 | -0.319 | -0.488 | -0.149 | -0.490 | -3.679 | 0.000 |
| d_compa_1 ~~ sa06_01_t1 | 0.074 | -0.011 | 0.160 | 0.181 | 1.704 | 0.088 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.189 | 4.080 | 4.298 | 4.400 | 75.546 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7683.038 | 7764.668 | 0.972 | 0.033 | 0.057 |
# parameters of interest
params_lcs_compa_ideal_hyp5 <- broom::tidy(fit_mi_lcs_compa_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 0.947 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.581 | NA | NA |
| d_compa_1 ~1 | 0.488 | -0.485 | 1.460 | 1.336 | 0.983 | 0.326 |
| compa_t1 ~1 | 4.293 | 4.199 | 4.387 | 7.216 | 89.548 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.127 | 0.005 | 0.250 | 0.955 | 2.032 | 0.042 |
| d_compa_1 ~ compa_t1 | -0.131 | -0.351 | 0.090 | -0.213 | -1.162 | 0.245 |
| d_compa_1 ~~ sa06_01_t1 | 0.026 | -0.045 | 0.096 | 0.075 | 0.709 | 0.478 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.594 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7732.912 | 7814.542 | 0.923 | 0.071 | 0.077 |
# parameters of interest
params_lcs_compa_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_compa_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sa07_04_t1", "sa07_04_t1 ~~ sa07_04_t1", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.098 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.687 | NA | NA |
| d_compa_1 ~1 | 1.223 | 0.497 | 1.949 | 2.651 | 3.302 | 0.001 |
| compa_t1 ~1 | 4.137 | 4.029 | 4.245 | 5.614 | 75.061 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.170 | 0.062 | 0.277 | 0.798 | 3.095 | 0.002 |
| d_compa_1 ~ compa_t1 | -0.281 | -0.448 | -0.115 | -0.449 | -3.309 | 0.001 |
| d_compa_1 ~~ sa07_04_t1 | 0.019 | -0.073 | 0.112 | 0.035 | 0.406 | 0.685 |
| sa07_04_t1 ~~ sa07_04_t1 | 1.729 | 1.536 | 1.923 | 1.000 | 17.558 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7872.214 | 7953.843 | 1 | 0 | 0.033 |
# parameters of interest
params_lcs_compa_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_compa_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sa07_04_t1", "sa07_04_t1 ~~ sa07_04_t1", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 0.941 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.570 | NA | NA |
| d_compa_1 ~1 | 0.440 | -0.504 | 1.384 | 1.231 | 0.913 | 0.361 |
| compa_t1 ~1 | 4.292 | 4.198 | 4.386 | 7.273 | 89.501 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.123 | 0.001 | 0.244 | 0.961 | 1.984 | 0.047 |
| d_compa_1 ~ compa_t1 | -0.119 | -0.333 | 0.095 | -0.197 | -1.092 | 0.275 |
| d_compa_1 ~~ sa07_04_t1 | -0.067 | -0.167 | 0.032 | -0.146 | -1.334 | 0.182 |
| sa07_04_t1 ~~ sa07_04_t1 | 1.729 | 1.536 | 1.923 | 1.000 | 17.557 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6642.515 | 6724.145 | 0.962 | 0.055 | 0.054 |
# parameters of interest
params_lcs_respe_curr_hyp5 <- broom::tidy(fit_mi_lcs_respe_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.993 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.535 | NA | NA |
| d_respe_1 ~1 | 0.640 | -0.094 | 1.374 | 2.654 | 1.710 | 0.087 |
| respe_t1 ~1 | 4.477 | 4.405 | 4.550 | 10.003 | 121.509 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.054 | 0.015 | 0.094 | 0.935 | 2.713 | 0.007 |
| d_respe_1 ~ respe_t1 | -0.138 | -0.298 | 0.022 | -0.256 | -1.686 | 0.092 |
| d_respe_1 ~~ sa06_01_t1 | 0.006 | -0.037 | 0.049 | 0.027 | 0.270 | 0.787 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.299 | 4.401 | 75.581 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6138.589 | 6220.219 | 0.951 | 0.05 | 0.062 |
# parameters of interest
params_lcs_respe_ideal_hyp5 <- broom::tidy(fit_mi_lcs_respe_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.868 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.559 | NA | NA |
| d_respe_1 ~1 | 0.191 | -0.644 | 1.027 | 0.956 | 0.449 | 0.654 |
| respe_t1 ~1 | 4.703 | 4.646 | 4.760 | 15.123 | 161.199 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.040 | 0.010 | 0.069 | 0.995 | 2.645 | 0.008 |
| d_respe_1 ~ respe_t1 | -0.044 | -0.218 | 0.131 | -0.068 | -0.490 | 0.624 |
| d_respe_1 ~~ sa06_01_t1 | -0.010 | -0.048 | 0.028 | -0.052 | -0.517 | 0.605 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.561 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6823.519 | 6905.148 | 0.928 | 0.077 | 0.093 |
# parameters of interest
params_lcs_respe_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_respe_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sa07_05_t1", "sa07_05_t1 ~~ sa07_05_t1", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.990 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.532 | NA | NA |
| d_respe_1 ~1 | 0.625 | -0.126 | 1.376 | 2.599 | 1.630 | 0.103 |
| respe_t1 ~1 | 4.477 | 4.405 | 4.550 | 10.000 | 121.477 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.054 | 0.015 | 0.093 | 0.937 | 2.724 | 0.006 |
| d_respe_1 ~ respe_t1 | -0.134 | -0.299 | 0.030 | -0.250 | -1.603 | 0.109 |
| d_respe_1 ~~ sa07_05_t1 | 0.003 | -0.058 | 0.063 | 0.008 | 0.081 | 0.935 |
| sa07_05_t1 ~~ sa07_05_t1 | 1.674 | 1.379 | 1.968 | 1.000 | 11.142 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6318.586 | 6400.216 | 0.978 | 0.033 | 0.049 |
# parameters of interest
params_lcs_respe_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_respe_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sa07_05_t1", "sa07_05_t1 ~~ sa07_05_t1", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.875 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.571 | NA | NA |
| d_respe_1 ~1 | 0.269 | -0.565 | 1.103 | 1.331 | 0.633 | 0.527 |
| respe_t1 ~1 | 4.703 | 4.646 | 4.760 | 15.175 | 161.281 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.041 | 0.011 | 0.070 | 0.991 | 2.690 | 0.007 |
| d_respe_1 ~ respe_t1 | -0.060 | -0.235 | 0.114 | -0.092 | -0.676 | 0.499 |
| d_respe_1 ~~ sa07_05_t1 | -0.028 | -0.082 | 0.026 | -0.107 | -1.012 | 0.311 |
| sa07_05_t1 ~~ sa07_05_t1 | 1.674 | 1.379 | 1.968 | 1.000 | 11.143 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7684.064 | 7765.694 | 0.968 | 0.051 | 0.062 |
# parameters of interest
params_lcs_trust_curr_hyp5 <- broom::tidy(fit_mi_lcs_trust_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.054 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.546 | NA | NA |
| d_trust_1 ~1 | 0.469 | 0.104 | 0.833 | 1.620 | 2.521 | 0.012 |
| trust_t1 ~1 | 2.889 | 2.768 | 3.010 | 5.167 | 46.789 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.073 | 0.003 | 0.143 | 0.874 | 2.047 | 0.041 |
| d_trust_1 ~ trust_t1 | -0.184 | -0.315 | -0.052 | -0.355 | -2.742 | 0.006 |
| d_trust_1 ~~ sa06_01_t1 | 0.001 | -0.047 | 0.048 | 0.003 | 0.031 | 0.976 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.582 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7419.656 | 7501.286 | 0.955 | 0.048 | 0.06 |
# parameters of interest
params_lcs_trust_ideal_hyp5 <- broom::tidy(fit_mi_lcs_trust_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.996 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.678 | NA | NA |
| d_trust_1 ~1 | 0.468 | 0.152 | 0.784 | 1.675 | 2.905 | 0.004 |
| trust_t1 ~1 | 1.907 | 1.808 | 2.006 | 4.650 | 37.866 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.069 | 0.021 | 0.118 | 0.888 | 2.804 | 0.005 |
| d_trust_1 ~ trust_t1 | -0.228 | -0.391 | -0.064 | -0.334 | -2.735 | 0.006 |
| d_trust_1 ~~ sa06_01_t1 | -0.030 | -0.076 | 0.016 | -0.120 | -1.293 | 0.196 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.189 | 4.080 | 4.298 | 4.400 | 75.588 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7912.466 | 7994.095 | 0.984 | 0.035 | 0.059 |
# parameters of interest
params_lcs_trust_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_trust_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sa07_06_t1", "sa07_06_t1 ~~ sa07_06_t1", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.065 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.562 | NA | NA |
| d_trust_1 ~1 | 0.512 | 0.159 | 0.865 | 1.740 | 2.841 | 0.004 |
| trust_t1 ~1 | 2.889 | 2.768 | 3.010 | 5.183 | 46.790 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.074 | 0.005 | 0.143 | 0.858 | 2.115 | 0.034 |
| d_trust_1 ~ trust_t1 | -0.199 | -0.325 | -0.072 | -0.376 | -3.069 | 0.002 |
| d_trust_1 ~~ sa07_06_t1 | 0.062 | -0.003 | 0.128 | 0.162 | 1.858 | 0.063 |
| sa07_06_t1 ~~ sa07_06_t1 | 1.987 | 1.779 | 2.195 | 1.000 | 18.703 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7651.868 | 7733.497 | 0.98 | 0.031 | 0.044 |
# parameters of interest
params_lcs_trust_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_trust_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sa07_06_t1", "sa07_06_t1 ~~ sa07_06_t1", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.979 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.652 | NA | NA |
| d_trust_1 ~1 | 0.415 | 0.111 | 0.720 | 1.541 | 2.671 | 0.008 |
| trust_t1 ~1 | 1.907 | 1.809 | 2.006 | 4.713 | 37.912 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.066 | 0.020 | 0.112 | 0.910 | 2.793 | 0.005 |
| d_trust_1 ~ trust_t1 | -0.200 | -0.357 | -0.043 | -0.301 | -2.497 | 0.013 |
| d_trust_1 ~~ sa07_06_t1 | 0.034 | -0.033 | 0.101 | 0.093 | 0.991 | 0.322 |
| sa07_06_t1 ~~ sa07_06_t1 | 1.987 | 1.779 | 2.195 | 1.000 | 18.706 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7320.975 | 7402.604 | 0.984 | 0.049 | 0.031 |
# parameters of interest
params_lcs_organ_curr_hyp5 <- broom::tidy(fit_mi_lcs_organ_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.079 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.431 | NA | NA |
| d_organ_1 ~1 | 0.301 | 0.107 | 0.494 | 0.681 | 3.046 | 0.002 |
| organ_t1 ~1 | 2.967 | 2.812 | 3.122 | 2.683 | 37.558 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.168 | 0.083 | 0.252 | 0.859 | 3.899 | 0.000 |
| d_organ_1 ~ organ_t1 | -0.150 | -0.219 | -0.081 | -0.376 | -4.245 | 0.000 |
| d_organ_1 ~~ sa06_01_t1 | 0.019 | -0.043 | 0.080 | 0.048 | 0.596 | 0.551 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.444 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6263.329 | 6344.959 | 0.928 | 0.054 | 0.055 |
# parameters of interest
params_lcs_organ_ideal_hyp5 <- broom::tidy(fit_mi_lcs_organ_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.008 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.780 | NA | NA |
| d_organ_1 ~1 | 0.518 | 0.039 | 0.997 | 2.329 | 2.120 | 0.034 |
| organ_t1 ~1 | 1.682 | 1.590 | 1.774 | 5.853 | 35.962 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.042 | 0.001 | 0.083 | 0.843 | 2.000 | 0.045 |
| d_organ_1 ~ organ_t1 | -0.307 | -0.599 | -0.016 | -0.397 | -2.064 | 0.039 |
| d_organ_1 ~~ sa06_01_t1 | 0.021 | -0.015 | 0.057 | 0.107 | 1.126 | 0.260 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.082 | 4.299 | 4.401 | 75.685 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7595.791 | 7677.42 | 0.89 | 0.136 | 0.181 |
# parameters of interest
params_lcs_organ_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_organ_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sa07_07_t1", "sa07_07_t1 ~~ sa07_07_t1", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.141 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.504 | NA | NA |
| d_organ_1 ~1 | 0.489 | 0.230 | 0.748 | 1.000 | 3.706 | 0.000 |
| organ_t1 ~1 | 2.967 | 2.812 | 3.122 | 2.678 | 37.555 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.183 | 0.094 | 0.272 | 0.766 | 4.040 | 0.000 |
| d_organ_1 ~ organ_t1 | -0.214 | -0.301 | -0.126 | -0.484 | -4.770 | 0.000 |
| d_organ_1 ~~ sa07_07_t1 | 0.167 | 0.031 | 0.303 | 0.255 | 2.410 | 0.016 |
| sa07_07_t1 ~~ sa07_07_t1 | 2.345 | 2.152 | 2.537 | 1.000 | 23.859 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6543.486 | 6625.115 | 0.927 | 0.054 | 0.056 |
# parameters of interest
params_lcs_organ_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_organ_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sa07_07_t1", "sa07_07_t1 ~~ sa07_07_t1", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.023 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.813 | NA | NA |
| d_organ_1 ~1 | 0.570 | 0.108 | 1.031 | 2.484 | 2.418 | 0.016 |
| organ_t1 ~1 | 1.682 | 1.590 | 1.774 | 5.826 | 35.965 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.043 | 0.001 | 0.085 | 0.819 | 1.999 | 0.046 |
| d_organ_1 ~ organ_t1 | -0.338 | -0.619 | -0.057 | -0.425 | -2.357 | 0.018 |
| d_organ_1 ~~ sa07_07_t1 | 0.036 | -0.025 | 0.097 | 0.114 | 1.159 | 0.246 |
| sa07_07_t1 ~~ sa07_07_t1 | 2.345 | 2.152 | 2.538 | 1.000 | 23.855 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7394.18 | 7475.81 | 0.988 | 0.036 | 0.038 |
# parameters of interest
params_lcs_produ_curr_hyp5 <- broom::tidy(fit_mi_lcs_produ_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.994 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.455 | NA | NA |
| d_produ_1 ~1 | 0.152 | -0.189 | 0.494 | 0.412 | 0.875 | 0.382 |
| produ_t1 ~1 | 3.481 | 3.346 | 3.616 | 4.313 | 50.517 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.130 | 0.057 | 0.203 | 0.953 | 3.482 | 0.000 |
| d_produ_1 ~ produ_t1 | -0.099 | -0.195 | -0.003 | -0.217 | -2.019 | 0.043 |
| d_produ_1 ~~ sa06_01_t1 | -0.013 | -0.066 | 0.040 | -0.039 | -0.489 | 0.625 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.299 | 4.401 | 75.575 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 5863.013 | 5944.642 | 0.869 | 0.067 | 0.066 |
# parameters of interest
params_lcs_produ_ideal_hyp5 <- broom::tidy(fit_mi_lcs_produ_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.960 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.637 | NA | NA |
| d_produ_1 ~1 | 0.258 | -0.147 | 0.664 | 1.236 | 1.249 | 0.212 |
| produ_t1 ~1 | 1.592 | 1.514 | 1.670 | 5.053 | 40.151 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.041 | 0.005 | 0.076 | 0.929 | 2.252 | 0.024 |
| d_produ_1 ~ produ_t1 | -0.177 | -0.447 | 0.093 | -0.267 | -1.286 | 0.199 |
| d_produ_1 ~~ sa06_01_t1 | -0.005 | -0.041 | 0.030 | -0.027 | -0.288 | 0.774 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.299 | 4.401 | 75.518 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7642.167 | 7723.797 | 0.897 | 0.113 | 0.153 |
# parameters of interest
params_lcs_produ_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_produ_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sa07_08_t1", "sa07_08_t1 ~~ sa07_08_t1", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.038 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.504 | NA | NA |
| d_produ_1 ~1 | 0.344 | -0.091 | 0.779 | 0.881 | 1.552 | 0.121 |
| produ_t1 ~1 | 3.480 | 3.345 | 3.615 | 4.328 | 50.545 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.137 | 0.061 | 0.214 | 0.900 | 3.510 | 0.000 |
| d_produ_1 ~ produ_t1 | -0.154 | -0.277 | -0.031 | -0.317 | -2.446 | 0.014 |
| d_produ_1 ~~ sa07_08_t1 | 0.099 | -0.023 | 0.221 | 0.183 | 1.595 | 0.111 |
| sa07_08_t1 ~~ sa07_08_t1 | 2.121 | 1.919 | 2.323 | 1.000 | 20.589 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6113.862 | 6195.491 | 0.862 | 0.069 | 0.066 |
# parameters of interest
params_lcs_produ_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_produ_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sa07_08_t1", "sa07_08_t1 ~~ sa07_08_t1", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.959 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.634 | NA | NA |
| d_produ_1 ~1 | 0.255 | -0.149 | 0.660 | 1.225 | 1.238 | 0.216 |
| produ_t1 ~1 | 1.592 | 1.514 | 1.670 | 5.053 | 40.150 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.040 | 0.005 | 0.076 | 0.930 | 2.225 | 0.026 |
| d_produ_1 ~ produ_t1 | -0.175 | -0.444 | 0.094 | -0.265 | -1.277 | 0.202 |
| d_produ_1 ~~ sa07_08_t1 | -0.004 | -0.059 | 0.051 | -0.013 | -0.135 | 0.892 |
| sa07_08_t1 ~~ sa07_08_t1 | 2.121 | 1.919 | 2.323 | 1.000 | 20.590 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7417.81 | 7499.44 | 0.906 | 0.088 | 0.084 |
# parameters of interest
params_lcs_respo_curr_hyp5 <- broom::tidy(fit_mi_lcs_respo_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.039 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.406 | NA | NA |
| d_respo_1 ~1 | 0.518 | 0.067 | 0.968 | 2.054 | 2.252 | 0.024 |
| respo_t1 ~1 | 3.641 | 3.530 | 3.751 | 5.652 | 64.676 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.058 | 0.007 | 0.109 | 0.917 | 2.238 | 0.025 |
| d_respo_1 ~ respo_t1 | -0.113 | -0.235 | 0.009 | -0.289 | -1.821 | 0.069 |
| d_respo_1 ~~ sa06_01_t1 | 0.012 | -0.040 | 0.063 | 0.051 | 0.445 | 0.657 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.299 | 4.401 | 75.561 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6742.385 | 6824.014 | 0.985 | 0.025 | 0.046 |
# parameters of interest
params_lcs_respo_ideal_hyp5 <- broom::tidy(fit_mi_lcs_respo_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.981 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.722 | NA | NA |
| d_respo_1 ~1 | 1.102 | 0.338 | 1.867 | 3.050 | 2.826 | 0.005 |
| respo_t1 ~1 | 4.272 | 4.163 | 4.380 | 8.692 | 76.845 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.115 | 0.042 | 0.189 | 0.883 | 3.073 | 0.002 |
| d_respo_1 ~ respo_t1 | -0.251 | -0.426 | -0.076 | -0.342 | -2.816 | 0.005 |
| d_respo_1 ~~ sa06_01_t1 | -0.009 | -0.071 | 0.053 | -0.027 | -0.278 | 0.781 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.564 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7652.303 | 7733.933 | 0.851 | 0.116 | 0.124 |
# parameters of interest
params_lcs_respo_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_respo_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sa07_09_t1", "sa07_09_t1 ~~ sa07_09_t1", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.120 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.519 | NA | NA |
| d_respo_1 ~1 | 0.864 | 0.138 | 1.590 | 2.970 | 2.331 | 0.020 |
| respo_t1 ~1 | 3.641 | 3.531 | 3.752 | 5.798 | 64.563 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.067 | 0.012 | 0.123 | 0.798 | 2.368 | 0.018 |
| d_respo_1 ~ respo_t1 | -0.208 | -0.416 | -0.001 | -0.450 | -1.970 | 0.049 |
| d_respo_1 ~~ sa07_09_t1 | -0.178 | -0.279 | -0.076 | -0.471 | -3.430 | 0.001 |
| sa07_09_t1 ~~ sa07_09_t1 | 2.112 | 1.904 | 2.320 | 1.000 | 19.874 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6990.902 | 7072.531 | 0.995 | 0.015 | 0.041 |
# parameters of interest
params_lcs_respo_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_respo_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sa07_09_t1", "sa07_09_t1 ~~ sa07_09_t1", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.985 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.728 | NA | NA |
| d_respo_1 ~1 | 1.130 | 0.374 | 1.885 | 3.119 | 2.931 | 0.003 |
| respo_t1 ~1 | 4.272 | 4.163 | 4.381 | 8.720 | 76.866 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.115 | 0.042 | 0.189 | 0.879 | 3.077 | 0.002 |
| d_respo_1 ~ respo_t1 | -0.258 | -0.430 | -0.085 | -0.348 | -2.922 | 0.003 |
| d_respo_1 ~~ sa07_09_t1 | -0.045 | -0.135 | 0.045 | -0.092 | -0.983 | 0.326 |
| sa07_09_t1 ~~ sa07_09_t1 | 2.111 | 1.903 | 2.318 | 1.000 | 19.927 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7743.825 | 7825.454 | 0.978 | 0.043 | 0.058 |
# parameters of interest
params_lcs_anxie_curr_hyp5 <- broom::tidy(fit_mi_lcs_anxie_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.957 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.457 | NA | NA |
| d_anxie_1 ~1 | 0.362 | 0.082 | 0.643 | 0.844 | 2.532 | 0.011 |
| anxie_t1 ~1 | 2.660 | 2.522 | 2.799 | 2.962 | 37.559 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.181 | 0.076 | 0.285 | 0.980 | 3.379 | 0.001 |
| d_anxie_1 ~ anxie_t1 | -0.068 | -0.178 | 0.042 | -0.142 | -1.211 | 0.226 |
| d_anxie_1 ~~ sa06_01_t1 | 0.026 | -0.053 | 0.104 | 0.064 | 0.646 | 0.518 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.591 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7370.866 | 7452.496 | 0.912 | 0.041 | 0.049 |
# parameters of interest
params_lcs_anxie_ideal_hyp5 <- broom::tidy(fit_mi_lcs_anxie_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.819 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.878 | NA | NA |
| d_anxie_1 ~1 | 1.468 | -0.673 | 3.609 | 4.847 | 1.344 | 0.179 |
| anxie_t1 ~1 | 4.583 | 4.517 | 4.648 | 16.215 | 137.681 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.083 | -0.017 | 0.184 | 0.906 | 1.620 | 0.105 |
| d_anxie_1 ~ anxie_t1 | -0.329 | -0.790 | 0.132 | -0.307 | -1.397 | 0.162 |
| d_anxie_1 ~~ sa06_01_t1 | 0.019 | -0.028 | 0.066 | 0.071 | 0.811 | 0.418 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.600 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.604 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7991.586 | 8073.216 | 0.938 | 0.074 | 0.101 |
# parameters of interest
params_lcs_anxie_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_anxie_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sa07_10_t1", "sa07_10_t1 ~~ sa07_10_t1", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.967 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.469 | NA | NA |
| d_anxie_1 ~1 | 0.402 | 0.105 | 0.699 | 0.924 | 2.652 | 0.008 |
| anxie_t1 ~1 | 2.661 | 2.522 | 2.800 | 2.970 | 37.568 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.184 | 0.077 | 0.290 | 0.971 | 3.380 | 0.001 |
| d_anxie_1 ~ anxie_t1 | -0.083 | -0.198 | 0.032 | -0.171 | -1.416 | 0.157 |
| d_anxie_1 ~~ sa07_10_t1 | -0.023 | -0.154 | 0.108 | -0.037 | -0.348 | 0.728 |
| sa07_10_t1 ~~ sa07_10_t1 | 2.097 | 1.899 | 2.295 | 1.000 | 20.800 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7618.844 | 7700.474 | 0.879 | 0.049 | 0.05 |
# parameters of interest
params_lcs_anxie_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_anxie_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sa07_10_t1", "sa07_10_t1 ~~ sa07_10_t1", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.813 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.873 | NA | NA |
| d_anxie_1 ~1 | 1.426 | -0.748 | 3.601 | 4.625 | 1.286 | 0.198 |
| anxie_t1 ~1 | 4.583 | 4.517 | 4.648 | 15.948 | 136.899 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.087 | -0.019 | 0.192 | 0.911 | 1.611 | 0.107 |
| d_anxie_1 ~ anxie_t1 | -0.320 | -0.788 | 0.148 | -0.298 | -1.339 | 0.181 |
| d_anxie_1 ~~ sa07_10_t1 | 0.011 | -0.100 | 0.121 | 0.025 | 0.187 | 0.852 |
| sa07_10_t1 ~~ sa07_10_t1 | 2.097 | 1.900 | 2.295 | 1.000 | 20.800 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7475.641 | 7557.27 | 0.961 | 0.074 | 0.066 |
# parameters of interest
params_lcs_depre_curr_hyp5 <- broom::tidy(fit_mi_lcs_depre_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.013 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.522 | NA | NA |
| d_depre_1 ~1 | 0.623 | 0.370 | 0.876 | 1.567 | 4.825 | 0.000 |
| depre_t1 ~1 | 2.896 | 2.761 | 3.030 | 3.760 | 42.277 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.145 | 0.077 | 0.214 | 0.920 | 4.148 | 0.000 |
| d_depre_1 ~ depre_t1 | -0.146 | -0.229 | -0.062 | -0.282 | -3.410 | 0.001 |
| d_depre_1 ~~ sa06_01_t1 | 0.021 | -0.035 | 0.077 | 0.058 | 0.744 | 0.457 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.092 | 1.000 | 9.596 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.082 | 4.299 | 4.401 | 75.702 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6024.435 | 6106.065 | 0.894 | 0.066 | 0.066 |
# parameters of interest
params_lcs_depre_ideal_hyp5 <- broom::tidy(fit_mi_lcs_depre_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.919 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.539 | NA | NA |
| d_depre_1 ~1 | 0.356 | -0.875 | 1.588 | 2.617 | 0.567 | 0.571 |
| depre_t1 ~1 | 4.410 | 4.345 | 4.476 | 18.981 | 132.535 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.018 | -0.007 | 0.044 | 0.981 | 1.407 | 0.159 |
| d_depre_1 ~ depre_t1 | -0.080 | -0.356 | 0.196 | -0.137 | -0.569 | 0.569 |
| d_depre_1 ~~ sa06_01_t1 | 0.000 | -0.025 | 0.026 | 0.002 | 0.021 | 0.983 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.299 | 4.401 | 75.534 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7739.163 | 7820.793 | 0.919 | 0.11 | 0.131 |
# parameters of interest
params_lcs_depre_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_depre_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sa07_11_t1", "sa07_11_t1 ~~ sa07_11_t1", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.041 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.554 | NA | NA |
| d_depre_1 ~1 | 0.725 | 0.404 | 1.046 | 1.767 | 4.421 | 0.000 |
| depre_t1 ~1 | 2.896 | 2.761 | 3.030 | 3.758 | 42.269 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.149 | 0.078 | 0.220 | 0.885 | 4.093 | 0.000 |
| d_depre_1 ~ depre_t1 | -0.181 | -0.285 | -0.076 | -0.340 | -3.398 | 0.001 |
| d_depre_1 ~~ sa07_11_t1 | -0.067 | -0.188 | 0.055 | -0.116 | -1.077 | 0.282 |
| sa07_11_t1 ~~ sa07_11_t1 | 2.224 | 2.030 | 2.418 | 1.000 | 22.477 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6288.607 | 6370.236 | 0.886 | 0.069 | 0.07 |
# parameters of interest
params_lcs_depre_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_depre_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sa07_11_t1", "sa07_11_t1 ~~ sa07_11_t1", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.922 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.544 | NA | NA |
| d_depre_1 ~1 | 0.384 | -0.862 | 1.631 | 2.803 | 0.604 | 0.546 |
| depre_t1 ~1 | 4.410 | 4.345 | 4.476 | 18.986 | 132.534 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.018 | -0.007 | 0.044 | 0.979 | 1.423 | 0.155 |
| d_depre_1 ~ depre_t1 | -0.086 | -0.366 | 0.193 | -0.147 | -0.607 | 0.544 |
| d_depre_1 ~~ sa07_11_t1 | -0.015 | -0.060 | 0.029 | -0.076 | -0.685 | 0.493 |
| sa07_11_t1 ~~ sa07_11_t1 | 2.223 | 2.029 | 2.417 | 1.000 | 22.479 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7593.855 | 7675.484 | 0.988 | 0.038 | 0.059 |
# parameters of interest
params_lcs_volat_curr_hyp5 <- broom::tidy(fit_mi_lcs_volat_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.070 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.612 | NA | NA |
| d_volat_1 ~1 | 0.607 | 0.299 | 0.915 | 1.109 | 3.861 | 0.000 |
| volat_t1 ~1 | 3.221 | 3.078 | 3.364 | 3.364 | 44.115 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.252 | 0.135 | 0.370 | 0.843 | 4.195 | 0.000 |
| d_volat_1 ~ volat_t1 | -0.227 | -0.322 | -0.131 | -0.396 | -4.668 | 0.000 |
| d_volat_1 ~~ sa06_01_t1 | -0.069 | -0.148 | 0.010 | -0.145 | -1.712 | 0.087 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.907 | 0.722 | 1.093 | 1.000 | 9.581 | 0.000 |
| sa06_01_t1 ~1 | 4.192 | 4.084 | 4.300 | 4.402 | 75.886 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6893.103 | 6974.732 | 0.934 | 0.047 | 0.054 |
# parameters of interest
params_lcs_volat_ideal_hyp5 <- broom::tidy(fit_mi_lcs_volat_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.841 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.703 | NA | NA |
| d_volat_1 ~1 | 0.284 | -0.143 | 0.712 | 0.808 | 1.305 | 0.192 |
| volat_t1 ~1 | 1.684 | 1.602 | 1.767 | 3.998 | 40.009 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.120 | 0.032 | 0.209 | 0.971 | 2.674 | 0.007 |
| d_volat_1 ~ volat_t1 | -0.143 | -0.403 | 0.117 | -0.171 | -1.078 | 0.281 |
| d_volat_1 ~~ sa06_01_t1 | -0.049 | -0.107 | 0.008 | -0.149 | -1.673 | 0.094 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.299 | 4.401 | 75.611 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7774.581 | 7856.21 | 0.967 | 0.065 | 0.085 |
# parameters of interest
params_lcs_volat_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_volat_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sa07_12_t1", "sa07_12_t1 ~~ sa07_12_t1", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.080 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.624 | NA | NA |
| d_volat_1 ~1 | 0.645 | 0.317 | 0.973 | 1.165 | 3.853 | 0.000 |
| volat_t1 ~1 | 3.221 | 3.078 | 3.364 | 3.360 | 44.096 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.254 | 0.136 | 0.373 | 0.829 | 4.200 | 0.000 |
| d_volat_1 ~ volat_t1 | -0.238 | -0.340 | -0.136 | -0.413 | -4.583 | 0.000 |
| d_volat_1 ~~ sa07_12_t1 | -0.016 | -0.135 | 0.103 | -0.024 | -0.260 | 0.795 |
| sa07_12_t1 ~~ sa07_12_t1 | 1.655 | 1.466 | 1.843 | 1.000 | 17.210 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7073.097 | 7154.727 | 0.934 | 0.047 | 0.051 |
# parameters of interest
params_lcs_volat_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_volat_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sa07_12_t1", "sa07_12_t1 ~~ sa07_12_t1", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.834 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.691 | NA | NA |
| d_volat_1 ~1 | 0.253 | -0.181 | 0.686 | 0.723 | 1.142 | 0.253 |
| volat_t1 ~1 | 1.684 | 1.601 | 1.766 | 3.993 | 40.013 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.119 | 0.029 | 0.210 | 0.978 | 2.584 | 0.010 |
| d_volat_1 ~ volat_t1 | -0.124 | -0.387 | 0.139 | -0.150 | -0.925 | 0.355 |
| d_volat_1 ~~ sa07_12_t1 | -0.016 | -0.094 | 0.062 | -0.036 | -0.403 | 0.687 |
| sa07_12_t1 ~~ sa07_12_t1 | 1.655 | 1.466 | 1.843 | 1.000 | 17.208 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7334.854 | 7416.484 | 0.961 | 0.048 | 0.057 |
# parameters of interest
params_lcs_curio_curr_hyp5 <- broom::tidy(fit_mi_lcs_curio_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.104 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.683 | NA | NA |
| d_curio_1 ~1 | 1.238 | 0.443 | 2.033 | 3.348 | 3.052 | 0.002 |
| curio_t1 ~1 | 4.108 | 4.009 | 4.207 | 6.875 | 81.628 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.109 | 0.037 | 0.180 | 0.793 | 2.955 | 0.003 |
| d_curio_1 ~ curio_t1 | -0.281 | -0.467 | -0.096 | -0.455 | -2.972 | 0.003 |
| d_curio_1 ~~ sa06_01_t1 | 0.006 | -0.051 | 0.062 | 0.018 | 0.200 | 0.842 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.655 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7260.109 | 7341.738 | 1 | 0 | 0.031 |
# parameters of interest
params_lcs_curio_ideal_hyp5 <- broom::tidy(fit_mi_lcs_curio_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.631 | NA | NA |
| d_curio_1 ~1 | 0.841 | -0.189 | 1.872 | 4.065 | 1.600 | 0.110 |
| curio_t1 ~1 | 4.518 | 4.448 | 4.588 | 14.051 | 126.015 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.039 | -0.007 | 0.086 | 0.916 | 1.648 | 0.099 |
| d_curio_1 ~ curio_t1 | -0.186 | -0.412 | 0.039 | -0.290 | -1.622 | 0.105 |
| d_curio_1 ~~ sa06_01_t1 | 0.015 | -0.023 | 0.054 | 0.081 | 0.778 | 0.437 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.600 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.299 | 4.401 | 75.612 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7527.651 | 7609.281 | 0.912 | 0.074 | 0.089 |
# parameters of interest
params_lcs_curio_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_curio_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sa07_13_t1", "sa07_13_t1 ~~ sa07_13_t1", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.149 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.757 | NA | NA |
| d_curio_1 ~1 | 1.472 | 0.674 | 2.269 | 3.747 | 3.618 | 0.000 |
| curio_t1 ~1 | 4.108 | 4.010 | 4.206 | 6.888 | 81.883 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.114 | 0.042 | 0.185 | 0.736 | 3.107 | 0.002 |
| d_curio_1 ~ curio_t1 | -0.338 | -0.525 | -0.151 | -0.514 | -3.549 | 0.000 |
| d_curio_1 ~~ sa07_13_t1 | -0.119 | -0.212 | -0.025 | -0.264 | -2.492 | 0.013 |
| sa07_13_t1 ~~ sa07_13_t1 | 1.787 | 1.551 | 2.024 | 1.000 | 14.808 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7456.466 | 7538.096 | 0.982 | 0.025 | 0.045 |
# parameters of interest
params_lcs_curio_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_curio_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sa07_13_t1", "sa07_13_t1 ~~ sa07_13_t1", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.987 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.648 | NA | NA |
| d_curio_1 ~1 | 0.912 | -0.085 | 1.909 | 4.253 | 1.793 | 0.073 |
| curio_t1 ~1 | 4.518 | 4.448 | 4.589 | 13.837 | 126.006 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.042 | -0.006 | 0.089 | 0.905 | 1.715 | 0.086 |
| d_curio_1 ~ curio_t1 | -0.202 | -0.420 | 0.015 | -0.308 | -1.821 | 0.069 |
| d_curio_1 ~~ sa07_13_t1 | -0.064 | -0.132 | 0.003 | -0.236 | -1.863 | 0.062 |
| sa07_13_t1 ~~ sa07_13_t1 | 1.786 | 1.550 | 2.023 | 1.000 | 14.798 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7424.942 | 7506.571 | 0.98 | 0.047 | 0.056 |
# parameters of interest
params_lcs_aesth_curr_hyp5 <- broom::tidy(fit_mi_lcs_aesth_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 1.107 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.608 | NA | NA |
| d_aesth_1 ~1 | 0.704 | 0.426 | 0.982 | 18.808 | 4.956 | 0.000 |
| aesth_t1 ~1 | 2.906 | 2.785 | 3.027 | 42.683 | 47.045 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | -0.004 | 0.006 | 0.805 | 0.452 | 0.651 |
| d_aesth_1 ~ aesth_t1 | -0.243 | -0.338 | -0.148 | -0.442 | -5.016 | 0.000 |
| d_aesth_1 ~~ sa06_01_t1 | -0.003 | -0.012 | 0.006 | -0.097 | -0.699 | 0.485 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.601 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.299 | 4.401 | 75.615 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7305.061 | 7386.69 | 0.969 | 0.051 | 0.07 |
# parameters of interest
params_lcs_aesth_ideal_hyp5 <- broom::tidy(fit_mi_lcs_aesth_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.957 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.661 | NA | NA |
| d_aesth_1 ~1 | 0.605 | 0.128 | 1.082 | 13.584 | 2.484 | 0.013 |
| aesth_t1 ~1 | 3.157 | 3.020 | 3.294 | 49.007 | 45.200 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.002 | -0.006 | 0.010 | 0.922 | 0.432 | 0.666 |
| d_aesth_1 ~ aesth_t1 | -0.192 | -0.341 | -0.044 | -0.279 | -2.539 | 0.011 |
| d_aesth_1 ~~ sa06_01_t1 | 0.002 | -0.005 | 0.009 | 0.050 | 0.586 | 0.558 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.602 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.082 | 4.299 | 4.401 | 75.680 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7641.241 | 7722.87 | 0.968 | 0.06 | 0.072 |
# parameters of interest
params_lcs_aesth_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_aesth_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sa07_14_t1", "sa07_14_t1 ~~ sa07_14_t1", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 1.100 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.599 | NA | NA |
| d_aesth_1 ~1 | 0.680 | 0.399 | 0.962 | 17.934 | 4.734 | 0.000 |
| aesth_t1 ~1 | 2.906 | 2.785 | 3.027 | 41.712 | 47.044 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | -0.004 | 0.006 | 0.814 | 0.461 | 0.645 |
| d_aesth_1 ~ aesth_t1 | -0.235 | -0.331 | -0.139 | -0.431 | -4.783 | 0.000 |
| d_aesth_1 ~~ sa07_14_t1 | 0.000 | -0.008 | 0.007 | -0.008 | -0.100 | 0.921 |
| sa07_14_t1 ~~ sa07_14_t1 | 1.877 | 1.681 | 2.073 | 1.000 | 18.768 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7517.688 | 7599.317 | 0.989 | 0.03 | 0.042 |
# parameters of interest
params_lcs_aesth_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_aesth_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sa07_14_t1", "sa07_14_t1 ~~ sa07_14_t1", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.950 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.653 | NA | NA |
| d_aesth_1 ~1 | 0.572 | 0.114 | 1.030 | 12.768 | 2.446 | 0.014 |
| aesth_t1 ~1 | 3.157 | 3.020 | 3.294 | 48.440 | 45.192 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.002 | -0.006 | 0.010 | 0.930 | 0.437 | 0.662 |
| d_aesth_1 ~ aesth_t1 | -0.182 | -0.325 | -0.039 | -0.265 | -2.499 | 0.012 |
| d_aesth_1 ~~ sa07_14_t1 | -0.007 | -0.025 | 0.011 | -0.119 | -0.770 | 0.441 |
| sa07_14_t1 ~~ sa07_14_t1 | 1.877 | 1.681 | 2.073 | 1.000 | 18.765 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7289.365 | 7370.994 | 0.98 | 0.042 | 0.045 |
# parameters of interest
params_lcs_imagi_curr_hyp5 <- broom::tidy(fit_mi_lcs_imagi_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.074 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.518 | NA | NA |
| d_imagi_1 ~1 | 0.760 | 0.303 | 1.216 | 2.193 | 3.263 | 0.001 |
| imagi_t1 ~1 | 3.614 | 3.509 | 3.720 | 5.034 | 67.008 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.103 | 0.037 | 0.169 | 0.856 | 3.056 | 0.002 |
| d_imagi_1 ~ imagi_t1 | -0.183 | -0.301 | -0.065 | -0.379 | -3.048 | 0.002 |
| d_imagi_1 ~~ sa06_01_t1 | 0.020 | -0.040 | 0.081 | 0.067 | 0.663 | 0.507 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.604 | 0.000 |
| sa06_01_t1 ~1 | 4.190 | 4.081 | 4.298 | 4.401 | 75.616 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 6764.378 | 6846.007 | 0.948 | 0.041 | 0.058 |
# parameters of interest
params_lcs_imagi_ideal_hyp5 <- broom::tidy(fit_mi_lcs_imagi_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.903 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.590 | NA | NA |
| d_imagi_1 ~1 | 0.433 | -0.831 | 1.698 | 1.933 | 0.672 | 0.502 |
| imagi_t1 ~1 | 4.608 | 4.543 | 4.672 | 13.443 | 140.690 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.049 | 0.009 | 0.089 | 0.976 | 2.387 | 0.017 |
| d_imagi_1 ~ imagi_t1 | -0.100 | -0.369 | 0.169 | -0.153 | -0.730 | 0.465 |
| d_imagi_1 ~~ sa06_01_t1 | -0.029 | -0.075 | 0.017 | -0.138 | -1.246 | 0.213 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.906 | 0.721 | 1.091 | 1.000 | 9.602 | 0.000 |
| sa06_01_t1 ~1 | 4.189 | 4.081 | 4.298 | 4.401 | 75.483 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7533.845 | 7615.474 | 0.938 | 0.076 | 0.101 |
# parameters of interest
params_lcs_imagi_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_imagi_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sa07_15_t1", "sa07_15_t1 ~~ sa07_15_t1", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.093 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.541 | NA | NA |
| d_imagi_1 ~1 | 0.836 | 0.386 | 1.287 | 2.354 | 3.639 | 0.000 |
| imagi_t1 ~1 | 3.614 | 3.509 | 3.720 | 5.035 | 67.045 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.105 | 0.039 | 0.170 | 0.830 | 3.128 | 0.002 |
| d_imagi_1 ~ imagi_t1 | -0.204 | -0.321 | -0.088 | -0.413 | -3.432 | 0.001 |
| d_imagi_1 ~~ sa07_15_t1 | -0.068 | -0.161 | 0.025 | -0.145 | -1.428 | 0.153 |
| sa07_15_t1 ~~ sa07_15_t1 | 2.088 | 1.892 | 2.283 | 1.000 | 20.919 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 22 | 7011.995 | 7093.624 | 0.972 | 0.03 | 0.053 |
# parameters of interest
params_lcs_imagi_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_imagi_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sa07_15_t1", "sa07_15_t1 ~~ sa07_15_t1", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.911 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.607 | NA | NA |
| d_imagi_1 ~1 | 0.521 | -0.735 | 1.778 | 2.297 | 0.813 | 0.416 |
| imagi_t1 ~1 | 4.607 | 4.543 | 4.672 | 13.513 | 140.770 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.050 | 0.009 | 0.090 | 0.968 | 2.419 | 0.016 |
| d_imagi_1 ~ imagi_t1 | -0.119 | -0.387 | 0.148 | -0.179 | -0.874 | 0.382 |
| d_imagi_1 ~~ sa07_15_t1 | -0.002 | -0.069 | 0.064 | -0.008 | -0.073 | 0.942 |
| sa07_15_t1 ~~ sa07_15_t1 | 2.087 | 1.891 | 2.283 | 1.000 | 20.904 | 0.000 |
Run models for all facets with a template & loop:
# create df for table:
df_table_hyp5 <- bind_rows(
#traits
params_lcs_extra_curr_hyp5 %>% filter(term=="d_extra_1 ~~ sa06_01_t1"),
params_lcs_extra_ideal_hyp5 %>% filter(term=="d_extra_1 ~~ sa06_01_t1"),
params_lcs_extra_curr_specif_hyp5 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_extra_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_agree_curr_hyp5 %>% filter(term=="d_agree_1 ~~ sa06_01_t1"),
params_lcs_agree_ideal_hyp5 %>% filter(term=="d_agree_1 ~~ sa06_01_t1"),
params_lcs_agree_curr_specif_hyp5 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_agree_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_consc_curr_hyp5 %>% filter(term=="d_consc_1 ~~ sa06_01_t1"),
params_lcs_consc_ideal_hyp5 %>% filter(term=="d_consc_1 ~~ sa06_01_t1"),
params_lcs_consc_curr_specif_hyp5 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_consc_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_neuro_curr_hyp5 %>% filter(term=="d_neuro_1 ~~ sa06_01_t1"),
params_lcs_neuro_ideal_hyp5 %>% filter(term=="d_neuro_1 ~~ sa06_01_t1"),
params_lcs_neuro_curr_specif_hyp5 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_neuro_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_openn_curr_hyp5 %>% filter(term=="d_openn_1 ~~ sa06_01_t1"),
params_lcs_openn_ideal_hyp5 %>% filter(term=="d_openn_1 ~~ sa06_01_t1"),
params_lcs_openn_curr_specif_hyp5 %>% filter(term=="goals ~~ d_openn_1"),
params_lcs_openn_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_openn_1"),
#facets
params_lcs_socia_curr_hyp5 %>% filter(term=="d_socia_1 ~~ sa06_01_t1"),
params_lcs_socia_ideal_hyp5 %>% filter(term=="d_socia_1 ~~ sa06_01_t1"),
params_lcs_socia_curr_specif_hyp5 %>% filter(term=="d_socia_1 ~~ sa07_01_t1"),
params_lcs_socia_ideal_specif_hyp5 %>% filter(term=="d_socia_1 ~~ sa07_01_t1"),
params_lcs_asser_curr_hyp5 %>% filter(term=="d_asser_1 ~~ sa06_01_t1"),
params_lcs_asser_ideal_hyp5 %>% filter(term=="d_asser_1 ~~ sa06_01_t1"),
params_lcs_asser_curr_specif_hyp5 %>% filter(term=="d_asser_1 ~~ sa07_02_t1"),
params_lcs_asser_ideal_specif_hyp5 %>% filter(term=="d_asser_1 ~~ sa07_02_t1"),
params_lcs_energ_curr_hyp5 %>% filter(term=="d_energ_1 ~~ sa06_01_t1"),
params_lcs_energ_ideal_hyp5 %>% filter(term=="d_energ_1 ~~ sa06_01_t1"),
params_lcs_energ_curr_specif_hyp5 %>% filter(term=="d_energ_1 ~~ sa07_03_t1"),
params_lcs_energ_ideal_specif_hyp5 %>% filter(term=="d_energ_1 ~~ sa07_03_t1"),
params_lcs_compa_curr_hyp5 %>% filter(term=="d_compa_1 ~~ sa06_01_t1"),
params_lcs_compa_ideal_hyp5 %>% filter(term=="d_compa_1 ~~ sa06_01_t1"),
params_lcs_compa_curr_specif_hyp5 %>% filter(term=="d_compa_1 ~~ sa07_04_t1"),
params_lcs_compa_ideal_specif_hyp5 %>% filter(term=="d_compa_1 ~~ sa07_04_t1"),
params_lcs_respe_curr_hyp5 %>% filter(term=="d_respe_1 ~~ sa06_01_t1"),
params_lcs_respe_ideal_hyp5 %>% filter(term=="d_respe_1 ~~ sa06_01_t1"),
params_lcs_respe_curr_specif_hyp5 %>% filter(term=="d_respe_1 ~~ sa07_05_t1"),
params_lcs_respe_ideal_specif_hyp5 %>% filter(term=="d_respe_1 ~~ sa07_05_t1"),
params_lcs_trust_curr_hyp5 %>% filter(term=="d_trust_1 ~~ sa06_01_t1"),
params_lcs_trust_ideal_hyp5 %>% filter(term=="d_trust_1 ~~ sa06_01_t1"),
params_lcs_trust_curr_specif_hyp5 %>% filter(term=="d_trust_1 ~~ sa07_06_t1"),
params_lcs_trust_ideal_specif_hyp5 %>% filter(term=="d_trust_1 ~~ sa07_06_t1"),
params_lcs_organ_curr_hyp5 %>% filter(term=="d_organ_1 ~~ sa06_01_t1"),
params_lcs_organ_ideal_hyp5 %>% filter(term=="d_organ_1 ~~ sa06_01_t1"),
params_lcs_organ_curr_specif_hyp5 %>% filter(term=="d_organ_1 ~~ sa07_07_t1"),
params_lcs_organ_ideal_specif_hyp5 %>% filter(term=="d_organ_1 ~~ sa07_07_t1"),
params_lcs_produ_curr_hyp5 %>% filter(term=="d_produ_1 ~~ sa06_01_t1"),
params_lcs_produ_ideal_hyp5 %>% filter(term=="d_produ_1 ~~ sa06_01_t1"),
params_lcs_produ_curr_specif_hyp5 %>% filter(term=="d_produ_1 ~~ sa07_08_t1"),
params_lcs_produ_ideal_specif_hyp5 %>% filter(term=="d_produ_1 ~~ sa07_08_t1"),
params_lcs_respo_curr_hyp5 %>% filter(term=="d_respo_1 ~~ sa06_01_t1"),
params_lcs_respo_ideal_hyp5 %>% filter(term=="d_respo_1 ~~ sa06_01_t1"),
params_lcs_respo_curr_specif_hyp5 %>% filter(term=="d_respo_1 ~~ sa07_09_t1"),
params_lcs_respo_ideal_specif_hyp5 %>% filter(term=="d_respo_1 ~~ sa07_09_t1"),
params_lcs_anxie_curr_hyp5 %>% filter(term=="d_anxie_1 ~~ sa06_01_t1"),
params_lcs_anxie_ideal_hyp5 %>% filter(term=="d_anxie_1 ~~ sa06_01_t1"),
params_lcs_anxie_curr_specif_hyp5 %>% filter(term=="d_anxie_1 ~~ sa07_10_t1"),
params_lcs_anxie_ideal_specif_hyp5 %>% filter(term=="d_anxie_1 ~~ sa07_10_t1"),
params_lcs_depre_curr_hyp5 %>% filter(term=="d_depre_1 ~~ sa06_01_t1"),
params_lcs_depre_ideal_hyp5 %>% filter(term=="d_depre_1 ~~ sa06_01_t1"),
params_lcs_depre_curr_specif_hyp5 %>% filter(term=="d_depre_1 ~~ sa07_11_t1"),
params_lcs_depre_ideal_specif_hyp5 %>% filter(term=="d_depre_1 ~~ sa07_11_t1"),
params_lcs_volat_curr_hyp5 %>% filter(term=="d_volat_1 ~~ sa06_01_t1"),
params_lcs_volat_ideal_hyp5 %>% filter(term=="d_volat_1 ~~ sa06_01_t1"),
params_lcs_volat_curr_specif_hyp5 %>% filter(term=="d_volat_1 ~~ sa07_12_t1"),
params_lcs_volat_ideal_specif_hyp5 %>% filter(term=="d_volat_1 ~~ sa07_12_t1"),
params_lcs_curio_curr_hyp5 %>% filter(term=="d_curio_1 ~~ sa06_01_t1"),
params_lcs_curio_ideal_hyp5 %>% filter(term=="d_curio_1 ~~ sa06_01_t1"),
params_lcs_curio_curr_specif_hyp5 %>% filter(term=="d_curio_1 ~~ sa07_13_t1"),
params_lcs_curio_ideal_specif_hyp5 %>% filter(term=="d_curio_1 ~~ sa07_13_t1"),
params_lcs_aesth_curr_hyp5 %>% filter(term=="d_aesth_1 ~~ sa06_01_t1"),
params_lcs_aesth_ideal_hyp5 %>% filter(term=="d_aesth_1 ~~ sa06_01_t1"),
params_lcs_aesth_curr_specif_hyp5 %>% filter(term=="d_aesth_1 ~~ sa07_14_t1"),
params_lcs_aesth_ideal_specif_hyp5 %>% filter(term=="d_aesth_1 ~~ sa07_14_t1"),
params_lcs_imagi_curr_hyp5 %>% filter(term=="d_imagi_1 ~~ sa06_01_t1"),
params_lcs_imagi_ideal_hyp5 %>% filter(term=="d_imagi_1 ~~ sa06_01_t1"),
params_lcs_imagi_curr_specif_hyp5 %>% filter(term=="d_imagi_1 ~~ sa07_15_t1"),
params_lcs_imagi_ideal_specif_hyp5 %>% filter(term=="d_imagi_1 ~~ sa07_15_t1"),
) %>%
mutate(trait = rep(names(b5_vars), each=4),
ref = rep(rep(c("current", "ideal"), 2), 20),
goal = rep(c(rep("general", 2), rep("specific", 2)), 20)) %>%
select(trait, ref, goal, estimate, std.all, statistic, p.value)Results summary across the Big Five traits: covariance of the latent change score and acceptance goal(s)
kable(df_table_hyp5[1:20, ], digits = 3)| trait | ref | goal | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extraversion | current | general | 0.047 | 0.140 | 2.273 | 0.023 |
| extraversion | ideal | general | 0.018 | 0.081 | 1.133 | 0.257 |
| extraversion | current | specific | 0.024 | 0.082 | 0.739 | 0.460 |
| extraversion | ideal | specific | -0.035 | -0.179 | -1.543 | 0.123 |
| agreeableness | current | general | 0.029 | 0.150 | 1.548 | 0.122 |
| agreeableness | ideal | general | 0.001 | 0.003 | 0.037 | 0.970 |
| agreeableness | current | specific | 0.002 | 0.011 | 0.105 | 0.916 |
| agreeableness | ideal | specific | -0.013 | -0.057 | -0.548 | 0.583 |
| conscientiousness | current | general | -0.003 | -0.008 | -0.126 | 0.899 |
| conscientiousness | ideal | general | -0.022 | -0.080 | -1.189 | 0.235 |
| conscientiousness | current | specific | -0.115 | -0.300 | -2.610 | 0.009 |
| conscientiousness | ideal | specific | -0.040 | -0.120 | -1.485 | 0.137 |
| neuroticism | current | general | -0.052 | -0.131 | -1.725 | 0.085 |
| neuroticism | ideal | general | -0.017 | -0.060 | -0.802 | 0.422 |
| neuroticism | current | specific | 0.035 | 0.097 | 1.042 | 0.297 |
| neuroticism | ideal | specific | 0.045 | 0.183 | 2.622 | 0.009 |
| openness | current | general | 0.003 | 0.013 | 0.174 | 0.862 |
| openness | ideal | general | 0.050 | 0.187 | 1.799 | 0.072 |
| openness | current | specific | -0.030 | -0.151 | -1.683 | 0.092 |
| openness | ideal | specific | -0.036 | -0.220 | -2.098 | 0.036 |
Results summary across the Big Five facets: covariance of the latent change score and acceptance goal(s)
kable(df_table_hyp5[21:80, ], digits = 3)| trait | ref | goal | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| sociability | current | general | 0.062 | 0.177 | 2.239 | 0.025 |
| sociability | ideal | general | -0.005 | -0.020 | -0.239 | 0.811 |
| sociability | current | specific | -0.012 | -0.024 | -0.257 | 0.797 |
| sociability | ideal | specific | -0.045 | -0.133 | -0.986 | 0.324 |
| assertiveness | current | general | 0.044 | 0.144 | 1.738 | 0.082 |
| assertiveness | ideal | general | 0.017 | 0.100 | 1.059 | 0.290 |
| assertiveness | current | specific | -0.004 | -0.010 | -0.106 | 0.916 |
| assertiveness | ideal | specific | -0.021 | -0.082 | -0.856 | 0.392 |
| energy | current | general | -0.013 | -0.050 | -0.684 | 0.494 |
| energy | ideal | general | -0.014 | -0.144 | -1.038 | 0.299 |
| energy | current | specific | 0.047 | 0.125 | 1.344 | 0.179 |
| energy | ideal | specific | 0.013 | 0.096 | 0.752 | 0.452 |
| compassion | current | general | 0.074 | 0.181 | 1.704 | 0.088 |
| compassion | ideal | general | 0.026 | 0.075 | 0.709 | 0.478 |
| compassion | current | specific | 0.019 | 0.035 | 0.406 | 0.685 |
| compassion | ideal | specific | -0.067 | -0.146 | -1.334 | 0.182 |
| respectfulness | current | general | 0.006 | 0.027 | 0.270 | 0.787 |
| respectfulness | ideal | general | -0.010 | -0.052 | -0.517 | 0.605 |
| respectfulness | current | specific | 0.003 | 0.008 | 0.081 | 0.935 |
| respectfulness | ideal | specific | -0.028 | -0.107 | -1.012 | 0.311 |
| trust | current | general | 0.001 | 0.003 | 0.031 | 0.976 |
| trust | ideal | general | -0.030 | -0.120 | -1.293 | 0.196 |
| trust | current | specific | 0.062 | 0.162 | 1.858 | 0.063 |
| trust | ideal | specific | 0.034 | 0.093 | 0.991 | 0.322 |
| organization | current | general | 0.019 | 0.048 | 0.596 | 0.551 |
| organization | ideal | general | 0.021 | 0.107 | 1.126 | 0.260 |
| organization | current | specific | 0.167 | 0.255 | 2.410 | 0.016 |
| organization | ideal | specific | 0.036 | 0.114 | 1.159 | 0.246 |
| productiveness | current | general | -0.013 | -0.039 | -0.489 | 0.625 |
| productiveness | ideal | general | -0.005 | -0.027 | -0.288 | 0.774 |
| productiveness | current | specific | 0.099 | 0.183 | 1.595 | 0.111 |
| productiveness | ideal | specific | -0.004 | -0.013 | -0.135 | 0.892 |
| responsibility | current | general | 0.012 | 0.051 | 0.445 | 0.657 |
| responsibility | ideal | general | -0.009 | -0.027 | -0.278 | 0.781 |
| responsibility | current | specific | -0.178 | -0.471 | -3.430 | 0.001 |
| responsibility | ideal | specific | -0.045 | -0.092 | -0.983 | 0.326 |
| anxiety | current | general | 0.026 | 0.064 | 0.646 | 0.518 |
| anxiety | ideal | general | 0.019 | 0.071 | 0.811 | 0.418 |
| anxiety | current | specific | -0.023 | -0.037 | -0.348 | 0.728 |
| anxiety | ideal | specific | 0.011 | 0.025 | 0.187 | 0.852 |
| depression | current | general | 0.021 | 0.058 | 0.744 | 0.457 |
| depression | ideal | general | 0.000 | 0.002 | 0.021 | 0.983 |
| depression | current | specific | -0.067 | -0.116 | -1.077 | 0.282 |
| depression | ideal | specific | -0.015 | -0.076 | -0.685 | 0.493 |
| volatility | current | general | -0.069 | -0.145 | -1.712 | 0.087 |
| volatility | ideal | general | -0.049 | -0.149 | -1.673 | 0.094 |
| volatility | current | specific | -0.016 | -0.024 | -0.260 | 0.795 |
| volatility | ideal | specific | -0.016 | -0.036 | -0.403 | 0.687 |
| curiosity | current | general | 0.006 | 0.018 | 0.200 | 0.842 |
| curiosity | ideal | general | 0.015 | 0.081 | 0.778 | 0.437 |
| curiosity | current | specific | -0.119 | -0.264 | -2.492 | 0.013 |
| curiosity | ideal | specific | -0.064 | -0.236 | -1.863 | 0.062 |
| aesthetic | current | general | -0.003 | -0.097 | -0.699 | 0.485 |
| aesthetic | ideal | general | 0.002 | 0.050 | 0.586 | 0.558 |
| aesthetic | current | specific | 0.000 | -0.008 | -0.100 | 0.921 |
| aesthetic | ideal | specific | -0.007 | -0.119 | -0.770 | 0.441 |
| imagination | current | general | 0.020 | 0.067 | 0.663 | 0.507 |
| imagination | ideal | general | -0.029 | -0.138 | -1.246 | 0.213 |
| imagination | current | specific | -0.068 | -0.145 | -1.428 | 0.153 |
| imagination | ideal | specific | -0.002 | -0.008 | -0.073 | 0.942 |
Prepare data frame for plotting:
df_table_hyp5_plot <- bind_rows(
#traits
params_lcs_extra_curr_hyp5 %>% filter(term=="d_extra_1 ~~ sa06_01_t1"),
params_lcs_extra_ideal_hyp5 %>% filter(term=="d_extra_1 ~~ sa06_01_t1"),
params_lcs_extra_curr_specif_hyp5 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_extra_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_agree_curr_hyp5 %>% filter(term=="d_agree_1 ~~ sa06_01_t1"),
params_lcs_agree_ideal_hyp5 %>% filter(term=="d_agree_1 ~~ sa06_01_t1"),
params_lcs_agree_curr_specif_hyp5 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_agree_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_consc_curr_hyp5 %>% filter(term=="d_consc_1 ~~ sa06_01_t1"),
params_lcs_consc_ideal_hyp5 %>% filter(term=="d_consc_1 ~~ sa06_01_t1"),
params_lcs_consc_curr_specif_hyp5 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_consc_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_neuro_curr_hyp5 %>% filter(term=="d_neuro_1 ~~ sa06_01_t1"),
params_lcs_neuro_ideal_hyp5 %>% filter(term=="d_neuro_1 ~~ sa06_01_t1"),
params_lcs_neuro_curr_specif_hyp5 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_neuro_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_openn_curr_hyp5 %>% filter(term=="d_openn_1 ~~ sa06_01_t1"),
params_lcs_openn_ideal_hyp5 %>% filter(term=="d_openn_1 ~~ sa06_01_t1"),
params_lcs_openn_curr_specif_hyp5 %>% filter(term=="goals ~~ d_openn_1"),
params_lcs_openn_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_openn_1"),
#facets
params_lcs_socia_curr_hyp5 %>% filter(term=="d_socia_1 ~~ sa06_01_t1"),
params_lcs_socia_ideal_hyp5 %>% filter(term=="d_socia_1 ~~ sa06_01_t1"),
params_lcs_socia_curr_specif_hyp5 %>% filter(term=="d_socia_1 ~~ sa07_01_t1"),
params_lcs_socia_ideal_specif_hyp5 %>% filter(term=="d_socia_1 ~~ sa07_01_t1"),
params_lcs_asser_curr_hyp5 %>% filter(term=="d_asser_1 ~~ sa06_01_t1"),
params_lcs_asser_ideal_hyp5 %>% filter(term=="d_asser_1 ~~ sa06_01_t1"),
params_lcs_asser_curr_specif_hyp5 %>% filter(term=="d_asser_1 ~~ sa07_02_t1"),
params_lcs_asser_ideal_specif_hyp5 %>% filter(term=="d_asser_1 ~~ sa07_02_t1"),
params_lcs_energ_curr_hyp5 %>% filter(term=="d_energ_1 ~~ sa06_01_t1"),
params_lcs_energ_ideal_hyp5 %>% filter(term=="d_energ_1 ~~ sa06_01_t1"),
params_lcs_energ_curr_specif_hyp5 %>% filter(term=="d_energ_1 ~~ sa07_03_t1"),
params_lcs_energ_ideal_specif_hyp5 %>% filter(term=="d_energ_1 ~~ sa07_03_t1"),
params_lcs_compa_curr_hyp5 %>% filter(term=="d_compa_1 ~~ sa06_01_t1"),
params_lcs_compa_ideal_hyp5 %>% filter(term=="d_compa_1 ~~ sa06_01_t1"),
params_lcs_compa_curr_specif_hyp5 %>% filter(term=="d_compa_1 ~~ sa07_04_t1"),
params_lcs_compa_ideal_specif_hyp5 %>% filter(term=="d_compa_1 ~~ sa07_04_t1"),
params_lcs_respe_curr_hyp5 %>% filter(term=="d_respe_1 ~~ sa06_01_t1"),
params_lcs_respe_ideal_hyp5 %>% filter(term=="d_respe_1 ~~ sa06_01_t1"),
params_lcs_respe_curr_specif_hyp5 %>% filter(term=="d_respe_1 ~~ sa07_05_t1"),
params_lcs_respe_ideal_specif_hyp5 %>% filter(term=="d_respe_1 ~~ sa07_05_t1"),
params_lcs_trust_curr_hyp5 %>% filter(term=="d_trust_1 ~~ sa06_01_t1"),
params_lcs_trust_ideal_hyp5 %>% filter(term=="d_trust_1 ~~ sa06_01_t1"),
params_lcs_trust_curr_specif_hyp5 %>% filter(term=="d_trust_1 ~~ sa07_06_t1"),
params_lcs_trust_ideal_specif_hyp5 %>% filter(term=="d_trust_1 ~~ sa07_06_t1"),
params_lcs_organ_curr_hyp5 %>% filter(term=="d_organ_1 ~~ sa06_01_t1"),
params_lcs_organ_ideal_hyp5 %>% filter(term=="d_organ_1 ~~ sa06_01_t1"),
params_lcs_organ_curr_specif_hyp5 %>% filter(term=="d_organ_1 ~~ sa07_07_t1"),
params_lcs_organ_ideal_specif_hyp5 %>% filter(term=="d_organ_1 ~~ sa07_07_t1"),
params_lcs_produ_curr_hyp5 %>% filter(term=="d_produ_1 ~~ sa06_01_t1"),
params_lcs_produ_ideal_hyp5 %>% filter(term=="d_produ_1 ~~ sa06_01_t1"),
params_lcs_produ_curr_specif_hyp5 %>% filter(term=="d_produ_1 ~~ sa07_08_t1"),
params_lcs_produ_ideal_specif_hyp5 %>% filter(term=="d_produ_1 ~~ sa07_08_t1"),
params_lcs_respo_curr_hyp5 %>% filter(term=="d_respo_1 ~~ sa06_01_t1"),
params_lcs_respo_ideal_hyp5 %>% filter(term=="d_respo_1 ~~ sa06_01_t1"),
params_lcs_respo_curr_specif_hyp5 %>% filter(term=="d_respo_1 ~~ sa07_09_t1"),
params_lcs_respo_ideal_specif_hyp5 %>% filter(term=="d_respo_1 ~~ sa07_09_t1"),
params_lcs_anxie_curr_hyp5 %>% filter(term=="d_anxie_1 ~~ sa06_01_t1"),
params_lcs_anxie_ideal_hyp5 %>% filter(term=="d_anxie_1 ~~ sa06_01_t1"),
params_lcs_anxie_curr_specif_hyp5 %>% filter(term=="d_anxie_1 ~~ sa07_10_t1"),
params_lcs_anxie_ideal_specif_hyp5 %>% filter(term=="d_anxie_1 ~~ sa07_10_t1"),
params_lcs_depre_curr_hyp5 %>% filter(term=="d_depre_1 ~~ sa06_01_t1"),
params_lcs_depre_ideal_hyp5 %>% filter(term=="d_depre_1 ~~ sa06_01_t1"),
params_lcs_depre_curr_specif_hyp5 %>% filter(term=="d_depre_1 ~~ sa07_11_t1"),
params_lcs_depre_ideal_specif_hyp5 %>% filter(term=="d_depre_1 ~~ sa07_11_t1"),
params_lcs_volat_curr_hyp5 %>% filter(term=="d_volat_1 ~~ sa06_01_t1"),
params_lcs_volat_ideal_hyp5 %>% filter(term=="d_volat_1 ~~ sa06_01_t1"),
params_lcs_volat_curr_specif_hyp5 %>% filter(term=="d_volat_1 ~~ sa07_12_t1"),
params_lcs_volat_ideal_specif_hyp5 %>% filter(term=="d_volat_1 ~~ sa07_12_t1"),
params_lcs_curio_curr_hyp5 %>% filter(term=="d_curio_1 ~~ sa06_01_t1"),
params_lcs_curio_ideal_hyp5 %>% filter(term=="d_curio_1 ~~ sa06_01_t1"),
params_lcs_curio_curr_specif_hyp5 %>% filter(term=="d_curio_1 ~~ sa07_13_t1"),
params_lcs_curio_ideal_specif_hyp5 %>% filter(term=="d_curio_1 ~~ sa07_13_t1"),
params_lcs_aesth_curr_hyp5 %>% filter(term=="d_aesth_1 ~~ sa06_01_t1"),
params_lcs_aesth_ideal_hyp5 %>% filter(term=="d_aesth_1 ~~ sa06_01_t1"),
params_lcs_aesth_curr_specif_hyp5 %>% filter(term=="d_aesth_1 ~~ sa07_14_t1"),
params_lcs_aesth_ideal_specif_hyp5 %>% filter(term=="d_aesth_1 ~~ sa07_14_t1"),
params_lcs_imagi_curr_hyp5 %>% filter(term=="d_imagi_1 ~~ sa06_01_t1"),
params_lcs_imagi_ideal_hyp5 %>% filter(term=="d_imagi_1 ~~ sa06_01_t1"),
params_lcs_imagi_curr_specif_hyp5 %>% filter(term=="d_imagi_1 ~~ sa07_15_t1"),
params_lcs_imagi_ideal_specif_hyp5 %>% filter(term=="d_imagi_1 ~~ sa07_15_t1"),
) %>%
mutate(trait = rep(names(b5_vars), each=4),
ref = rep(rep(c("current", "ideal"), 2), 20),
goal = rep(c(rep("general", 2), rep("specific", 2)), 20)) %>%
select(trait, ref, goal, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp5_plot <- df_table_hyp5_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor(ref, levels = c("current", "ideal"), labels = c("current", "ideal"))) %>%
mutate(goal = factor(goal, levels = c("general", "specific"), labels = c("general", "specific"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_hyp5_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + goal, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
Desire to change and frequency of skill-building behaviors measured at the follow-up assessment will be positively related to change in current-self ratings in the skill-building group.
To test this hypothesis, we will estimate the mean-level difference in current trait ratings between baseline and follow up using a latent change model for each big five domain and facet. We will then include two moderators. The first will indicate how much the individual wanted to change on a given big five domain or facet. The second will indicate their frequency of skill-building behaviors. We will estimate the main effects of each of these variables and the interaction between these variables on the trait change score.
Reshape and split data set by intervention group:
df_sbsa_wide_pers_sb_mod <- df_sbsa %>%
filter(rando=="Skill-Building") %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sb07", # facet-specific change goals
"sb04"))) %>% # frequency skill-building behaviors
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with(c("sb07", "sb04")))) %>%
select(-c(sb07_01_t2, sb07_02_t2, sb07_03_t2, sb07_04_t2, sb07_05_t2,
sb07_06_t2, sb07_07_t2, sb07_08_t2, sb07_09_t2, sb07_10_t2,
sb07_11_t2, sb07_12_t2, sb07_13_t2, sb07_14_t2, sb07_15_t2,
sb04_01_t1, sb04_02_t1, sb04_03_t1)) # frequency of skill-building behaviors measured at T2
# colnames(df_sbsa_wide_pers_sb_mod)
group_assign <- df_sbsa %>% select(pid, rando) %>% unique()
df_sbsa_wide_pers_sb_mod <- df_sbsa_wide_pers %>% left_join(group_assign) %>% filter(rando=="Skill-Building") %>% select(-rando) %>%
left_join(df_sbsa_wide_pers_sb_mod)Run models for all traits with a template & loop:
# create templates:
# 1st, for facet-specific change goals
trait_template_mod_goal <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadings
goals =~ 1*ind_goal_1 + ind_goal_2 + ind_goal_3 # latent variable for moderator
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~ goals # This estimates the moderation effect on personality at T1
d_trait_1 ~ trait_t1 + goals # This estimates the self-feedback parameter and the moderation effect on the change score
goals ~ 0*1 # This fixes the intercept of the moderator to 0
goals ~~ goals # This estimates the variance of the moderator
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind_goal_1 ~~ ind_goal_1
ind_goal_2 ~~ ind_goal_2
ind_goal_3 ~~ ind_goal_3
ind_goal_1 ~ 1
ind_goal_2 ~ 1
ind_goal_3 ~ 1
'
trait_facets_nrs <- list(a1 = c(1:3), b2 = c(4:6), c3 = c(7:9), d4 = c(10:12), e5 = c(13:15)) # matching facet nrs to traits
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead!
mod_names = paste0("sb07_", str_pad(trait_facets_nrs[[i]], 2, pad = "0"), "_t1")
template_filled <- str_replace_all(trait_template_mod_goal,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"),
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3"),
"ind_goal_1" = mod_names[1], "ind_goal_2" = mod_names[2],
"ind_goal_3" = mod_names[3]))
trait_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), trait_model_fit))
}
# 2nd, for frequency of skill-building behavior
trait_template_mod_frequ <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for extra_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
frequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderator
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~ frequ # This estimates the moderation effect on personality at T1
d_trait_1 ~ trait_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb04_01_t2 ~~ sb04_01_t2
sb04_02_t2 ~~ sb04_02_t2
sb04_03_t2 ~~ sb04_03_t2
sb04_01_t2 ~ 1
sb04_02_t2 ~ 1
sb04_03_t2 ~ 1
'
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_mod_frequ,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"),
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), trait_model_fit))
} Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 26 | 6131.956 | 6229.687 | 0.979 | 0.056 | 0.041 |
# parameters of interest
params_lcs_extra_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_extra_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ goals", "goals ~~ goals", "extra_t1 ~ goals", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.995 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.570 | NA | NA |
| d_extra_1 ~1 | 0.447 | 0.124 | 0.769 | 1.343 | 2.713 | 0.007 |
| extra_t1 ~1 | 2.755 | 2.677 | 2.832 | 4.752 | 69.691 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.101 | 0.063 | 0.138 | 0.911 | 5.236 | 0.000 |
| extra_t1 ~ goals | -0.530 | -0.728 | -0.333 | -0.601 | -5.263 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.112 | -0.229 | 0.006 | -0.195 | -1.864 | 0.062 |
| d_extra_1 ~ goals | 0.070 | -0.076 | 0.215 | 0.138 | 0.939 | 0.348 |
| goals ~~ goals | 0.431 | 0.189 | 0.674 | 1.000 | 3.487 | 0.000 |
Results summary (frequ = frequency of skill building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 26 | 5856.675 | 5954.407 | 0.985 | 0.05 | 0.054 |
# parameters of interest
params_lcs_extra_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_extra_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ frequ", "frequ ~~ frequ", "extra_t1 ~ frequ", # frequency of skill building
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.994 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.569 | NA | NA |
| d_extra_1 ~1 | 0.616 | 0.348 | 0.884 | 1.864 | 4.503 | 0.000 |
| extra_t1 ~1 | 2.755 | 2.678 | 2.833 | 4.776 | 69.689 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.097 | 0.062 | 0.133 | 0.894 | 5.354 | 0.000 |
| extra_t1 ~ frequ | 0.127 | -0.020 | 0.274 | 0.156 | 1.699 | 0.089 |
| d_extra_1 ~ extra_t1 | -0.173 | -0.266 | -0.080 | -0.303 | -3.655 | 0.000 |
| d_extra_1 ~ frequ | 0.083 | -0.004 | 0.170 | 0.177 | 1.862 | 0.063 |
| frequ ~~ frequ | 0.498 | 0.337 | 0.660 | 1.000 | 6.040 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 26 | 5846.862 | 5944.593 | 0.98 | 0.048 | 0.045 |
# parameters of interest
params_lcs_agree_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_agree_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ goals", "goals ~~ goals", "agree_t1 ~ goals", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.058 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.541 | NA | NA |
| d_agree_1 ~1 | 0.648 | 0.338 | 0.957 | 2.993 | 4.103 | 0.000 |
| agree_t1 ~1 | 3.344 | 3.278 | 3.410 | 7.897 | 99.667 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.041 | 0.019 | 0.062 | 0.866 | 3.714 | 0.000 |
| agree_t1 ~ goals | -0.161 | -0.253 | -0.070 | -0.337 | -3.470 | 0.001 |
| d_agree_1 ~ agree_t1 | -0.171 | -0.263 | -0.080 | -0.335 | -3.657 | 0.000 |
| d_agree_1 ~ goals | 0.018 | -0.035 | 0.070 | 0.072 | 0.661 | 0.509 |
| goals ~~ goals | 0.783 | 0.431 | 1.136 | 1.000 | 4.354 | 0.000 |
Results summary (frequ = frequency of skill building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 26 | 5426.782 | 5524.513 | 0.99 | 0.034 | 0.04 |
# parameters of interest
params_lcs_agree_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_agree_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ frequ", "frequ ~~ frequ", "agree_t1 ~ frequ", # frequency of skill building
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.055 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.541 | NA | NA |
| d_agree_1 ~1 | 0.769 | 0.459 | 1.079 | 3.492 | 4.859 | 0.000 |
| agree_t1 ~1 | 3.343 | 3.277 | 3.409 | 7.786 | 99.351 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.038 | 0.019 | 0.058 | 0.792 | 3.861 | 0.000 |
| agree_t1 ~ frequ | 0.099 | 0.010 | 0.188 | 0.167 | 2.180 | 0.029 |
| d_agree_1 ~ agree_t1 | -0.207 | -0.297 | -0.117 | -0.404 | -4.516 | 0.000 |
| d_agree_1 ~ frequ | 0.088 | 0.026 | 0.150 | 0.290 | 2.771 | 0.006 |
| frequ ~~ frequ | 0.525 | 0.362 | 0.688 | 1.000 | 6.305 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 26 | 5889.408 | 5987.139 | 0.987 | 0.054 | 0.03 |
# parameters of interest
params_lcs_consc_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_consc_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ goals", "goals ~~ goals", "consc_t1 ~ goals", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.041 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.452 | NA | NA |
| d_consc_1 ~1 | 0.468 | 0.148 | 0.788 | 1.450 | 2.870 | 0.004 |
| consc_t1 ~1 | 3.270 | 3.178 | 3.363 | 4.399 | 69.246 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.094 | 0.059 | 0.129 | 0.900 | 5.203 | 0.000 |
| consc_t1 ~ goals | -0.379 | -0.457 | -0.300 | -0.613 | -9.415 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.105 | -0.199 | -0.011 | -0.241 | -2.180 | 0.029 |
| d_consc_1 ~ goals | 0.028 | -0.030 | 0.086 | 0.105 | 0.949 | 0.343 |
| goals ~~ goals | 1.450 | 1.137 | 1.762 | 1.000 | 9.099 | 0.000 |
Results summary (frequ = frequency of skill building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 26 | 5609.39 | 5707.121 | 1 | 0 | 0.021 |
# parameters of interest
params_lcs_consc_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_consc_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ frequ", "frequ ~~ frequ", "consc_t1 ~ frequ", # frequency of skill building
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.041 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.453 | NA | NA |
| d_consc_1 ~1 | 0.595 | 0.367 | 0.823 | 1.847 | 5.111 | 0.000 |
| consc_t1 ~1 | 3.271 | 3.178 | 3.363 | 4.421 | 69.279 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.090 | 0.056 | 0.124 | 0.864 | 5.154 | 0.000 |
| consc_t1 ~ frequ | 0.121 | -0.030 | 0.273 | 0.117 | 1.573 | 0.116 |
| d_consc_1 ~ consc_t1 | -0.144 | -0.208 | -0.080 | -0.330 | -4.392 | 0.000 |
| d_consc_1 ~ frequ | 0.093 | 0.019 | 0.168 | 0.206 | 2.460 | 0.014 |
| frequ ~~ frequ | 0.506 | 0.344 | 0.668 | 1.000 | 6.123 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 26 | 6532.499 | 6630.23 | 0.978 | 0.068 | 0.033 |
# parameters of interest
params_lcs_neuro_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_neuro_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ goals", "goals ~~ goals", "neuro_t1 ~ goals", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.014 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.603 | NA | NA |
| d_neuro_1 ~1 | 0.683 | 0.202 | 1.163 | 1.349 | 2.784 | 0.005 |
| neuro_t1 ~1 | 3.533 | 3.433 | 3.632 | 4.155 | 69.490 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.226 | 0.157 | 0.295 | 0.884 | 6.423 | 0.000 |
| neuro_t1 ~ goals | 0.515 | 0.420 | 0.610 | 0.712 | 10.608 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.261 | -0.397 | -0.124 | -0.438 | -3.737 | 0.000 |
| d_neuro_1 ~ goals | 0.071 | -0.040 | 0.183 | 0.166 | 1.253 | 0.210 |
| goals ~~ goals | 1.379 | 1.052 | 1.706 | 1.000 | 8.270 | 0.000 |
Results summary (frequ = frequency of skill building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 26 | 6206.687 | 6304.419 | 0.984 | 0.056 | 0.048 |
# parameters of interest
params_lcs_neuro_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_neuro_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ frequ", "frequ ~~ frequ", "neuro_t1 ~ frequ", # frequency of skill building
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.015 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.603 | NA | NA |
| d_neuro_1 ~1 | 0.419 | 0.148 | 0.689 | 0.838 | 3.036 | 0.002 |
| neuro_t1 ~1 | 3.532 | 3.433 | 3.631 | 4.200 | 69.641 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.221 | 0.151 | 0.292 | 0.887 | 6.151 | 0.000 |
| neuro_t1 ~ frequ | 0.115 | -0.076 | 0.306 | 0.098 | 1.180 | 0.238 |
| d_neuro_1 ~ neuro_t1 | -0.185 | -0.262 | -0.109 | -0.312 | -4.736 | 0.000 |
| d_neuro_1 ~ frequ | -0.069 | -0.182 | 0.044 | -0.099 | -1.203 | 0.229 |
| frequ ~~ frequ | 0.511 | 0.345 | 0.678 | 1.000 | 6.025 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 26 | 5956.917 | 6054.648 | 0.983 | 0.05 | 0.044 |
# parameters of interest
params_lcs_openn_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_openn_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ goals", "goals ~~ goals", "openn_t1 ~ goals", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.914 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.395 | NA | NA |
| d_openn_1 ~1 | -0.120 | -0.452 | 0.211 | -0.639 | -0.712 | 0.477 |
| openn_t1 ~1 | 3.553 | 3.487 | 3.620 | 8.157 | 104.851 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.031 | 0.010 | 0.052 | 0.886 | 2.919 | 0.004 |
| openn_t1 ~ goals | -0.211 | -0.321 | -0.100 | -0.302 | -3.740 | 0.000 |
| d_openn_1 ~ openn_t1 | 0.052 | -0.041 | 0.144 | 0.119 | 1.089 | 0.276 |
| d_openn_1 ~ goals | 0.106 | 0.032 | 0.181 | 0.354 | 2.799 | 0.005 |
| goals ~~ goals | 0.391 | 0.211 | 0.572 | 1.000 | 4.245 | 0.000 |
Results summary (frequ = frequency of skill building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 26 | 5714.045 | 5811.776 | 0.995 | 0.025 | 0.026 |
# parameters of interest
params_lcs_openn_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_openn_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ frequ", "frequ ~~ frequ", "openn_t1 ~ frequ", # frequency of skill building
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.913 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.395 | NA | NA |
| d_openn_1 ~1 | 0.153 | -0.170 | 0.477 | 0.807 | 0.928 | 0.353 |
| openn_t1 ~1 | 3.553 | 3.486 | 3.619 | 8.089 | 104.824 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.033 | 0.013 | 0.054 | 0.920 | 3.168 | 0.002 |
| openn_t1 ~ frequ | 0.156 | 0.058 | 0.253 | 0.249 | 3.132 | 0.002 |
| d_openn_1 ~ openn_t1 | -0.025 | -0.116 | 0.065 | -0.058 | -0.546 | 0.585 |
| d_openn_1 ~ frequ | 0.079 | 0.008 | 0.150 | 0.291 | 2.167 | 0.030 |
| frequ ~~ frequ | 0.493 | 0.334 | 0.651 | 1.000 | 6.092 | 0.000 |
Run models for all facets with a template & loop:
# create templates:
# 1st, for facet-specific change goal
facet_template_mod_goal <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~ ind_goal # This estimates the moderation effect on personality at T1
d_facet_1 ~ facet_t1 + ind_goal # This estimates the self-feedback parameter and the moderation effect on the change score
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[5]], item_nrs)
mod_name = paste0("sb07_", str_pad(i-5, 2, pad = "0"), "_t1")
template_filled <- str_replace_all(facet_template_mod_goal,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = mod_name))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), facet_model_fit))
}
# 2nd, for frequency of skill-building behavior
facet_template_mod_frequ <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
frequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderator
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~ frequ # This estimates the moderation effect on personality at T1
d_facet_1 ~ facet_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
sb04_01_t2 ~~ sb04_01_t2
sb04_02_t2 ~~ sb04_02_t2
sb04_03_t2 ~~ sb04_03_t2
sb04_01_t2 ~ 1
sb04_02_t2 ~ 1
sb04_03_t2 ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[5]], item_nrs)
template_filled <- str_replace_all(facet_template_mod_frequ,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), facet_model_fit))
} Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7722.367 | 7808.821 | 0.958 | 0.072 | 0.048 |
# parameters of interest
params_lcs_socia_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_socia_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ sb07_01_t1", "sb07_01_t1 ~~ sb07_01_t1", "d_socia_1 ~ sb07_01_t1", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.013 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.633 | NA | NA |
| d_socia_1 ~1 | 0.863 | 0.371 | 1.355 | 1.613 | 3.439 | 0.001 |
| socia_t1 ~1 | 3.990 | 3.619 | 4.361 | 4.664 | 21.092 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.255 | 0.154 | 0.356 | 0.889 | 4.937 | 0.000 |
| d_socia_1 ~ socia_t1 | -0.217 | -0.328 | -0.105 | -0.346 | -3.816 | 0.000 |
| d_socia_1 ~ sb07_01_t1 | -0.013 | -0.083 | 0.056 | -0.030 | -0.374 | 0.708 |
| sb07_01_t1 ~~ sb07_01_t1 | 1.442 | 1.240 | 1.644 | 1.000 | 13.995 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9385.649 | 9498.416 | 0.959 | 0.063 | 0.053 |
# parameters of interest
params_lcs_socia_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_socia_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ frequ", "frequ ~~ frequ", "socia_t1 ~ frequ", # frequency of skill building
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.013 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.629 | NA | NA |
| d_socia_1 ~1 | 0.799 | 0.496 | 1.102 | 1.511 | 5.168 | 0.000 |
| socia_t1 ~1 | 2.781 | 2.648 | 2.913 | 3.268 | 41.034 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.247 | 0.148 | 0.347 | 0.885 | 4.873 | 0.000 |
| socia_t1 ~ frequ | 0.150 | -0.054 | 0.354 | 0.126 | 1.443 | 0.149 |
| d_socia_1 ~ socia_t1 | -0.212 | -0.312 | -0.111 | -0.341 | -4.122 | 0.000 |
| d_socia_1 ~ frequ | 0.055 | -0.077 | 0.186 | 0.074 | 0.813 | 0.416 |
| frequ ~~ frequ | 0.508 | 0.347 | 0.669 | 1.000 | 6.180 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 8086.648 | 8173.103 | 0.972 | 0.048 | 0.047 |
# parameters of interest
params_lcs_asser_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_asser_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ sb07_02_t1", "sb07_02_t1 ~~ sb07_02_t1", "d_asser_1 ~ sb07_02_t1", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.999 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.350 | NA | NA |
| d_asser_1 ~1 | 0.030 | -0.458 | 0.518 | 0.145 | 0.122 | 0.903 |
| asser_t1 ~1 | 3.588 | 3.275 | 3.901 | 6.000 | 22.450 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.041 | -0.007 | 0.088 | 0.926 | 1.674 | 0.094 |
| d_asser_1 ~ asser_t1 | -0.036 | -0.163 | 0.090 | -0.104 | -0.565 | 0.572 |
| d_asser_1 ~ sb07_02_t1 | 0.039 | -0.016 | 0.095 | 0.221 | 1.397 | 0.162 |
| sb07_02_t1 ~~ sb07_02_t1 | 1.379 | 1.196 | 1.561 | 1.000 | 14.829 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9720.475 | 9833.242 | 0.971 | 0.045 | 0.048 |
# parameters of interest
params_lcs_asser_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_asser_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ frequ", "frequ ~~ frequ", "asser_t1 ~ frequ", # frequency of skill building
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.997 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.351 | NA | NA |
| d_asser_1 ~1 | 0.263 | -0.081 | 0.607 | 1.308 | 1.496 | 0.135 |
| asser_t1 ~1 | 3.034 | 2.925 | 3.143 | 5.318 | 54.568 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.038 | -0.004 | 0.079 | 0.930 | 1.761 | 0.078 |
| asser_t1 ~ frequ | 0.112 | -0.029 | 0.252 | 0.139 | 1.552 | 0.121 |
| d_asser_1 ~ asser_t1 | -0.069 | -0.183 | 0.044 | -0.197 | -1.199 | 0.231 |
| d_asser_1 ~ frequ | 0.058 | -0.033 | 0.149 | 0.205 | 1.245 | 0.213 |
| frequ ~~ frequ | 0.508 | 0.347 | 0.670 | 1.000 | 6.177 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 8042.934 | 8129.388 | 0.945 | 0.072 | 0.049 |
# parameters of interest
params_lcs_energ_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_energ_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ sb07_03_t1", "sb07_03_t1 ~~ sb07_03_t1", "d_energ_1 ~ sb07_03_t1", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.993 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.563 | NA | NA |
| d_energ_1 ~1 | 0.311 | 0.033 | 0.589 | 1.316 | 2.195 | 0.028 |
| energ_t1 ~1 | 2.166 | 1.957 | 2.375 | 5.197 | 20.327 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.052 | 0.013 | 0.090 | 0.927 | 2.623 | 0.009 |
| d_energ_1 ~ energ_t1 | -0.157 | -0.275 | -0.039 | -0.277 | -2.599 | 0.009 |
| d_energ_1 ~ sb07_03_t1 | 0.003 | -0.029 | 0.035 | 0.015 | 0.177 | 0.860 |
| sb07_03_t1 ~~ sb07_03_t1 | 1.539 | 1.321 | 1.756 | 1.000 | 13.847 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9657.38 | 9770.147 | 0.954 | 0.059 | 0.055 |
# parameters of interest
params_lcs_energ_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_energ_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ frequ", "frequ ~~ frequ", "energ_t1 ~ frequ", # frequency of skill building
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.988 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.561 | NA | NA |
| d_energ_1 ~1 | 0.352 | 0.063 | 0.640 | 1.401 | 2.389 | 0.017 |
| energ_t1 ~1 | 2.681 | 2.574 | 2.789 | 6.061 | 48.922 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.057 | 0.018 | 0.095 | 0.898 | 2.872 | 0.004 |
| energ_t1 ~ frequ | -0.124 | -0.250 | 0.002 | -0.197 | -1.936 | 0.053 |
| d_energ_1 ~ energ_t1 | -0.170 | -0.278 | -0.061 | -0.299 | -3.069 | 0.002 |
| d_energ_1 ~ frequ | -0.066 | -0.149 | 0.017 | -0.185 | -1.556 | 0.120 |
| frequ ~~ frequ | 0.493 | 0.329 | 0.657 | 1.000 | 5.895 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7904.647 | 7991.102 | 0.996 | 0.016 | 0.036 |
# parameters of interest
params_lcs_compa_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_compa_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ sb07_04_t1", "sb07_04_t1 ~~ sb07_04_t1", "d_compa_1 ~ sb07_04_t1", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.147 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.348 | NA | NA |
| d_compa_1 ~1 | 0.742 | 0.128 | 1.357 | 3.576 | 2.366 | 0.018 |
| compa_t1 ~1 | 4.508 | 4.319 | 4.698 | 6.588 | 46.572 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.030 | -0.032 | 0.092 | 0.697 | 0.953 | 0.341 |
| d_compa_1 ~ compa_t1 | -0.162 | -0.290 | -0.033 | -0.532 | -2.471 | 0.013 |
| d_compa_1 ~ sb07_04_t1 | 0.009 | -0.039 | 0.057 | 0.059 | 0.360 | 0.719 |
| sb07_04_t1 ~~ sb07_04_t1 | 1.929 | 1.729 | 2.129 | 1.000 | 18.932 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9421.466 | 9534.233 | 0.991 | 0.023 | 0.044 |
# parameters of interest
params_lcs_compa_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_compa_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ frequ", "frequ ~~ frequ", "compa_t1 ~ frequ", # frequency of skill building
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.147 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.345 | NA | NA |
| d_compa_1 ~1 | 0.872 | 0.357 | 1.387 | 4.287 | 3.316 | 0.001 |
| compa_t1 ~1 | 4.176 | 4.081 | 4.270 | 6.177 | 86.302 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.022 | -0.036 | 0.081 | 0.536 | 0.742 | 0.458 |
| compa_t1 ~ frequ | 0.165 | 0.024 | 0.305 | 0.177 | 2.301 | 0.021 |
| d_compa_1 ~ compa_t1 | -0.187 | -0.307 | -0.068 | -0.622 | -3.076 | 0.002 |
| d_compa_1 ~ frequ | 0.114 | 0.022 | 0.206 | 0.409 | 2.436 | 0.015 |
| frequ ~~ frequ | 0.528 | 0.365 | 0.691 | 1.000 | 6.343 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7196.055 | 7282.51 | 0.952 | 0.066 | 0.052 |
# parameters of interest
params_lcs_respe_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_respe_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ sb07_05_t1", "sb07_05_t1 ~~ sb07_05_t1", "d_respe_1 ~ sb07_05_t1", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.097 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.599 | NA | NA |
| d_respe_1 ~1 | 0.886 | 0.263 | 1.509 | 2.743 | 2.788 | 0.005 |
| respe_t1 ~1 | 4.812 | 4.678 | 4.947 | 8.124 | 70.098 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.084 | 0.043 | 0.125 | 0.803 | 4.018 | 0.000 |
| d_respe_1 ~ respe_t1 | -0.204 | -0.330 | -0.078 | -0.374 | -3.166 | 0.002 |
| d_respe_1 ~ sb07_05_t1 | 0.031 | -0.018 | 0.081 | 0.130 | 1.255 | 0.209 |
| sb07_05_t1 ~~ sb07_05_t1 | 1.779 | 1.560 | 1.998 | 1.000 | 15.895 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 8772.712 | 8885.479 | 0.959 | 0.056 | 0.051 |
# parameters of interest
params_lcs_respe_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_respe_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ frequ", "frequ ~~ frequ", "respe_t1 ~ frequ", # frequency of skill building
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.097 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.600 | NA | NA |
| d_respe_1 ~1 | 1.147 | 0.625 | 1.668 | 3.607 | 4.310 | 0.000 |
| respe_t1 ~1 | 4.391 | 4.309 | 4.474 | 7.549 | 104.156 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.079 | 0.041 | 0.118 | 0.784 | 4.032 | 0.000 |
| respe_t1 ~ frequ | 0.109 | -0.017 | 0.235 | 0.134 | 1.689 | 0.091 |
| d_respe_1 ~ respe_t1 | -0.247 | -0.361 | -0.134 | -0.452 | -4.265 | 0.000 |
| d_respe_1 ~ frequ | 0.081 | 0.001 | 0.161 | 0.183 | 1.982 | 0.047 |
| frequ ~~ frequ | 0.517 | 0.354 | 0.679 | 1.000 | 6.227 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 8252.185 | 8338.64 | 0.94 | 0.067 | 0.052 |
# parameters of interest
params_lcs_trust_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_trust_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ sb07_06_t1", "sb07_06_t1 ~~ sb07_06_t1", "d_trust_1 ~ sb07_06_t1", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.031 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.562 | NA | NA |
| d_trust_1 ~1 | 0.414 | 0.011 | 0.816 | 1.249 | 2.015 | 0.044 |
| trust_t1 ~1 | 2.756 | 2.544 | 2.968 | 4.535 | 25.461 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.098 | 0.016 | 0.180 | 0.890 | 2.330 | 0.020 |
| d_trust_1 ~ trust_t1 | -0.175 | -0.315 | -0.035 | -0.321 | -2.454 | 0.014 |
| d_trust_1 ~ sb07_06_t1 | -0.014 | -0.062 | 0.035 | -0.055 | -0.550 | 0.583 |
| sb07_06_t1 ~~ sb07_06_t1 | 1.794 | 1.613 | 1.974 | 1.000 | 19.496 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9786.117 | 9898.884 | 0.963 | 0.05 | 0.046 |
# parameters of interest
params_lcs_trust_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_trust_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ frequ", "frequ ~~ frequ", "trust_t1 ~ frequ", # frequency of skill building
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.033 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.557 | NA | NA |
| d_trust_1 ~1 | 0.415 | 0.023 | 0.808 | 1.273 | 2.077 | 0.038 |
| trust_t1 ~1 | 2.894 | 2.773 | 3.015 | 4.778 | 46.927 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.090 | 0.013 | 0.166 | 0.842 | 2.302 | 0.021 |
| trust_t1 ~ frequ | -0.082 | -0.216 | 0.052 | -0.098 | -1.206 | 0.228 |
| d_trust_1 ~ trust_t1 | -0.189 | -0.329 | -0.049 | -0.350 | -2.645 | 0.008 |
| d_trust_1 ~ frequ | -0.102 | -0.209 | 0.004 | -0.226 | -1.883 | 0.060 |
| frequ ~~ frequ | 0.519 | 0.356 | 0.681 | 1.000 | 6.252 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7779.505 | 7865.959 | 0.951 | 0.086 | 0.044 |
# parameters of interest
params_lcs_organ_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_organ_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ sb07_07_t1", "sb07_07_t1 ~~ sb07_07_t1", "d_organ_1 ~ sb07_07_t1", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.086 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.521 | NA | NA |
| d_organ_1 ~1 | 0.413 | 0.203 | 0.622 | 0.778 | 3.858 | 0.000 |
| organ_t1 ~1 | 1.319 | 1.013 | 1.625 | 1.194 | 8.453 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.236 | 0.128 | 0.344 | 0.839 | 4.266 | 0.000 |
| d_organ_1 ~ organ_t1 | -0.176 | -0.283 | -0.069 | -0.367 | -3.227 | 0.001 |
| d_organ_1 ~ sb07_07_t1 | -0.019 | -0.096 | 0.057 | -0.053 | -0.490 | 0.624 |
| sb07_07_t1 ~~ sb07_07_t1 | 2.176 | 1.955 | 2.398 | 1.000 | 19.238 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9357.789 | 9470.556 | 0.988 | 0.035 | 0.034 |
# parameters of interest
params_lcs_organ_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_organ_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ frequ", "frequ ~~ frequ", "organ_t1 ~ frequ", # frequency of skill building
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.085 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.524 | NA | NA |
| d_organ_1 ~1 | 0.440 | 0.221 | 0.658 | 0.854 | 3.941 | 0.000 |
| organ_t1 ~1 | 2.819 | 2.671 | 2.966 | 2.646 | 37.423 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.215 | 0.119 | 0.312 | 0.814 | 4.376 | 0.000 |
| organ_t1 ~ frequ | -0.262 | -0.477 | -0.047 | -0.177 | -2.392 | 0.017 |
| d_organ_1 ~ organ_t1 | -0.207 | -0.289 | -0.125 | -0.428 | -4.949 | 0.000 |
| d_organ_1 ~ frequ | -0.122 | -0.247 | 0.003 | -0.170 | -1.907 | 0.057 |
| frequ ~~ frequ | 0.515 | 0.353 | 0.678 | 1.000 | 6.233 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7698.095 | 7784.549 | 0.975 | 0.053 | 0.036 |
# parameters of interest
params_lcs_produ_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_produ_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ sb07_08_t1", "sb07_08_t1 ~~ sb07_08_t1", "d_produ_1 ~ sb07_08_t1", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.044 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.478 | NA | NA |
| d_produ_1 ~1 | 0.352 | 0.032 | 0.671 | 0.995 | 2.159 | 0.031 |
| produ_t1 ~1 | 2.199 | 1.842 | 2.556 | 2.845 | 12.077 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.111 | 0.053 | 0.169 | 0.890 | 3.761 | 0.000 |
| d_produ_1 ~ produ_t1 | -0.122 | -0.240 | -0.003 | -0.266 | -2.005 | 0.045 |
| d_produ_1 ~ sb07_08_t1 | -0.029 | -0.086 | 0.028 | -0.107 | -1.006 | 0.314 |
| sb07_08_t1 ~~ sb07_08_t1 | 1.675 | 1.419 | 1.932 | 1.000 | 12.802 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9316.269 | 9429.036 | 0.973 | 0.049 | 0.045 |
# parameters of interest
params_lcs_produ_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_produ_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ frequ", "frequ ~~ frequ", "produ_t1 ~ frequ", # frequency of skill building
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.045 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.485 | NA | NA |
| d_produ_1 ~1 | 0.374 | 0.043 | 0.706 | 1.068 | 2.214 | 0.027 |
| produ_t1 ~1 | 3.354 | 3.224 | 3.484 | 4.437 | 50.536 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.102 | 0.049 | 0.156 | 0.833 | 3.785 | 0.000 |
| produ_t1 ~ frequ | -0.102 | -0.281 | 0.076 | -0.096 | -1.124 | 0.261 |
| d_produ_1 ~ produ_t1 | -0.161 | -0.261 | -0.060 | -0.346 | -3.140 | 0.002 |
| d_produ_1 ~ frequ | -0.125 | -0.219 | -0.031 | -0.253 | -2.605 | 0.009 |
| frequ ~~ frequ | 0.503 | 0.341 | 0.664 | 1.000 | 6.106 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7853.444 | 7939.898 | 0.924 | 0.084 | 0.081 |
# parameters of interest
params_lcs_respo_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_respo_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ sb07_09_t1", "sb07_09_t1 ~~ sb07_09_t1", "d_respo_1 ~ sb07_09_t1", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.998 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.436 | NA | NA |
| d_respo_1 ~1 | 0.375 | -0.109 | 0.858 | 1.875 | 1.519 | 0.129 |
| respo_t1 ~1 | 4.027 | 3.854 | 4.201 | 8.805 | 45.582 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.038 | 0.004 | 0.072 | 0.955 | 2.193 | 0.028 |
| d_respo_1 ~ respo_t1 | -0.091 | -0.207 | 0.025 | -0.209 | -1.542 | 0.123 |
| d_respo_1 ~ sb07_09_t1 | 0.001 | -0.031 | 0.033 | 0.010 | 0.082 | 0.935 |
| sb07_09_t1 ~~ sb07_09_t1 | 2.217 | 2.026 | 2.409 | 1.000 | 22.667 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9375.054 | 9487.821 | 0.946 | 0.063 | 0.07 |
# parameters of interest
params_lcs_respo_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_respo_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ frequ", "frequ ~~ frequ", "respo_t1 ~ frequ", # frequency of skill building
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.997 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.434 | NA | NA |
| d_respo_1 ~1 | 0.378 | 0.011 | 0.744 | 1.870 | 2.019 | 0.043 |
| respo_t1 ~1 | 3.619 | 3.517 | 3.721 | 7.806 | 69.636 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.038 | 0.004 | 0.073 | 0.942 | 2.177 | 0.029 |
| respo_t1 ~ frequ | -0.019 | -0.120 | 0.082 | -0.029 | -0.370 | 0.712 |
| d_respo_1 ~ respo_t1 | -0.091 | -0.189 | 0.008 | -0.208 | -1.797 | 0.072 |
| d_respo_1 ~ frequ | 0.032 | -0.034 | 0.098 | 0.113 | 0.951 | 0.342 |
| frequ ~~ frequ | 0.515 | 0.351 | 0.678 | 1.000 | 6.172 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 8149.672 | 8236.127 | 0.973 | 0.051 | 0.044 |
# parameters of interest
params_lcs_anxie_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_anxie_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ sb07_10_t1", "sb07_10_t1 ~~ sb07_10_t1", "d_anxie_1 ~ sb07_10_t1", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.075 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.664 | NA | NA |
| d_anxie_1 ~1 | 1.296 | 0.606 | 1.987 | 2.307 | 3.680 | 0.000 |
| anxie_t1 ~1 | 4.085 | 3.799 | 4.370 | 4.488 | 28.059 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.256 | 0.125 | 0.387 | 0.811 | 3.829 | 0.000 |
| d_anxie_1 ~ anxie_t1 | -0.316 | -0.475 | -0.157 | -0.511 | -3.889 | 0.000 |
| d_anxie_1 ~ sb07_10_t1 | -0.056 | -0.138 | 0.025 | -0.153 | -1.351 | 0.177 |
| sb07_10_t1 ~~ sb07_10_t1 | 2.317 | 2.075 | 2.560 | 1.000 | 18.745 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9719.042 | 9831.809 | 0.963 | 0.053 | 0.052 |
# parameters of interest
params_lcs_anxie_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_anxie_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ frequ", "frequ ~~ frequ", "anxie_t1 ~ frequ", # frequency of skill building
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.075 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.666 | NA | NA |
| d_anxie_1 ~1 | 0.916 | 0.561 | 1.271 | 1.623 | 5.057 | 0.000 |
| anxie_t1 ~1 | 2.780 | 2.645 | 2.915 | 3.051 | 40.363 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.261 | 0.116 | 0.405 | 0.817 | 3.531 | 0.000 |
| anxie_t1 ~ frequ | -0.206 | -0.420 | 0.007 | -0.163 | -1.897 | 0.058 |
| d_anxie_1 ~ anxie_t1 | -0.251 | -0.374 | -0.127 | -0.405 | -3.979 | 0.000 |
| d_anxie_1 ~ frequ | 0.068 | -0.082 | 0.218 | 0.086 | 0.883 | 0.377 |
| frequ ~~ frequ | 0.521 | 0.354 | 0.687 | 1.000 | 6.136 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7947.811 | 8034.265 | 0.963 | 0.076 | 0.053 |
# parameters of interest
params_lcs_depre_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_depre_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ sb07_11_t1", "sb07_11_t1 ~~ sb07_11_t1", "d_depre_1 ~ sb07_11_t1", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.959 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.546 | NA | NA |
| d_depre_1 ~1 | 0.689 | 0.100 | 1.278 | 1.867 | 2.291 | 0.022 |
| depre_t1 ~1 | 3.887 | 3.682 | 4.092 | 5.992 | 37.175 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.130 | 0.073 | 0.186 | 0.952 | 4.491 | 0.000 |
| d_depre_1 ~ depre_t1 | -0.156 | -0.299 | -0.013 | -0.275 | -2.138 | 0.032 |
| d_depre_1 ~ sb07_11_t1 | -0.022 | -0.081 | 0.036 | -0.095 | -0.741 | 0.459 |
| sb07_11_t1 ~~ sb07_11_t1 | 2.510 | 2.299 | 2.720 | 1.000 | 23.376 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9571.153 | 9683.92 | 0.951 | 0.073 | 0.06 |
# parameters of interest
params_lcs_depre_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_depre_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ frequ", "frequ ~~ frequ", "depre_t1 ~ frequ", # frequency of skill building
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.958 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.546 | NA | NA |
| d_depre_1 ~1 | 0.507 | 0.268 | 0.747 | 1.342 | 4.149 | 0.000 |
| depre_t1 ~1 | 2.931 | 2.807 | 3.055 | 4.424 | 46.350 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.137 | 0.076 | 0.197 | 0.956 | 4.431 | 0.000 |
| depre_t1 ~ frequ | -0.007 | -0.152 | 0.138 | -0.008 | -0.097 | 0.923 |
| d_depre_1 ~ depre_t1 | -0.118 | -0.198 | -0.037 | -0.206 | -2.876 | 0.004 |
| d_depre_1 ~ frequ | 0.021 | -0.071 | 0.113 | 0.040 | 0.444 | 0.657 |
| frequ ~~ frequ | 0.514 | 0.349 | 0.678 | 1.000 | 6.106 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 8117.701 | 8204.155 | 0.99 | 0.034 | 0.031 |
# parameters of interest
params_lcs_volat_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_volat_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ sb07_12_t1", "sb07_12_t1 ~~ sb07_12_t1", "d_volat_1 ~ sb07_12_t1", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.072 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.600 | NA | NA |
| d_volat_1 ~1 | 0.539 | 0.288 | 0.790 | 1.084 | 4.208 | 0.00 |
| volat_t1 ~1 | 2.625 | 2.352 | 2.898 | 2.953 | 18.817 | 0.00 |
| d_volat_1 ~~ d_volat_1 | 0.208 | 0.130 | 0.285 | 0.839 | 5.251 | 0.00 |
| d_volat_1 ~ volat_t1 | -0.207 | -0.292 | -0.122 | -0.370 | -4.764 | 0.00 |
| d_volat_1 ~ sb07_12_t1 | -0.028 | -0.094 | 0.039 | -0.073 | -0.824 | 0.41 |
| sb07_12_t1 ~~ sb07_12_t1 | 1.698 | 1.505 | 1.890 | 1.000 | 17.300 | 0.00 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9706.076 | 9818.843 | 0.973 | 0.051 | 0.043 |
# parameters of interest
params_lcs_volat_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_volat_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ frequ", "frequ ~~ frequ", "volat_t1 ~ frequ", # frequency of skill building
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.073 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.600 | NA | NA |
| d_volat_1 ~1 | 0.508 | 0.257 | 0.759 | 1.017 | 3.969 | 0.000 |
| volat_t1 ~1 | 3.257 | 3.124 | 3.389 | 3.650 | 48.093 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.208 | 0.132 | 0.283 | 0.833 | 5.387 | 0.000 |
| volat_t1 ~ frequ | 0.056 | -0.139 | 0.251 | 0.045 | 0.565 | 0.572 |
| d_volat_1 ~ volat_t1 | -0.220 | -0.295 | -0.144 | -0.393 | -5.694 | 0.000 |
| d_volat_1 ~ frequ | -0.067 | -0.194 | 0.060 | -0.096 | -1.033 | 0.302 |
| frequ ~~ frequ | 0.507 | 0.342 | 0.672 | 1.000 | 6.024 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7885.907 | 7972.362 | 0.968 | 0.048 | 0.047 |
# parameters of interest
params_lcs_curio_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_curio_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ sb07_13_t1", "sb07_13_t1 ~~ sb07_13_t1", "d_curio_1 ~ sb07_13_t1", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.027 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.349 | NA | NA |
| d_curio_1 ~1 | 0.375 | -0.224 | 0.974 | 2.213 | 1.228 | 0.219 |
| curio_t1 ~1 | 4.305 | 4.115 | 4.495 | 8.645 | 44.392 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.027 | -0.018 | 0.072 | 0.938 | 1.178 | 0.239 |
| d_curio_1 ~ curio_t1 | -0.080 | -0.209 | 0.048 | -0.236 | -1.228 | 0.219 |
| d_curio_1 ~ sb07_13_t1 | 0.005 | -0.047 | 0.058 | 0.040 | 0.201 | 0.841 |
| sb07_13_t1 ~~ sb07_13_t1 | 1.581 | 1.405 | 1.757 | 1.000 | 17.620 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9459.472 | 9572.239 | 0.966 | 0.046 | 0.049 |
# parameters of interest
params_lcs_curio_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_curio_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ frequ", "frequ ~~ frequ", "curio_t1 ~ frequ", # frequency of skill building
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.026 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.348 | NA | NA |
| d_curio_1 ~1 | 0.504 | 0.023 | 0.984 | 2.957 | 2.056 | 0.040 |
| curio_t1 ~1 | 4.064 | 3.967 | 4.161 | 8.089 | 82.153 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.023 | -0.019 | 0.065 | 0.793 | 1.068 | 0.285 |
| curio_t1 ~ frequ | 0.140 | 0.025 | 0.255 | 0.196 | 2.378 | 0.017 |
| d_curio_1 ~ curio_t1 | -0.109 | -0.225 | 0.008 | -0.320 | -1.826 | 0.068 |
| d_curio_1 ~ frequ | 0.095 | -0.006 | 0.196 | 0.392 | 1.846 | 0.065 |
| frequ ~~ frequ | 0.496 | 0.334 | 0.657 | 1.000 | 6.015 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 8171.241 | 8257.696 | 0.992 | 0.029 | 0.041 |
# parameters of interest
params_lcs_aesth_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_aesth_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ sb07_14_t1", "sb07_14_t1 ~~ sb07_14_t1", "d_aesth_1 ~ sb07_14_t1", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.979 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.510 | NA | NA |
| d_aesth_1 ~1 | 0.340 | 0.025 | 0.655 | 11.956 | 2.114 | 0.035 |
| aesth_t1 ~1 | 2.988 | 2.867 | 3.109 | 54.773 | 48.359 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 0.928 | 4.401 | 0.000 |
| d_aesth_1 ~ aesth_t1 | -0.116 | -0.220 | -0.011 | -0.222 | -2.170 | 0.030 |
| d_aesth_1 ~ sb07_14_t1 | 0.003 | 0.000 | 0.007 | 0.155 | 1.885 | 0.059 |
| sb07_14_t1 ~~ sb07_14_t1 | 1.863 | 1.682 | 2.043 | 1.000 | 20.247 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9691.47 | 9804.237 | 0.986 | 0.035 | 0.042 |
# parameters of interest
params_lcs_aesth_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_aesth_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ frequ", "frequ ~~ frequ", "aesth_t1 ~ frequ", # frequency of skill building
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.979 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.510 | NA | NA |
| d_aesth_1 ~1 | 0.392 | 0.062 | 0.723 | 15.698 | 2.326 | 0.020 |
| aesth_t1 ~1 | 2.990 | 2.870 | 3.110 | 62.434 | 48.916 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 0.921 | 4.426 | 0.000 |
| aesth_t1 ~ frequ | 0.012 | 0.001 | 0.023 | 0.175 | 2.074 | 0.038 |
| d_aesth_1 ~ aesth_t1 | -0.130 | -0.240 | -0.020 | -0.250 | -2.320 | 0.020 |
| d_aesth_1 ~ frequ | 0.006 | 0.000 | 0.013 | 0.180 | 1.948 | 0.051 |
| frequ ~~ frequ | 0.500 | 0.340 | 0.659 | 1.000 | 6.129 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7762.185 | 7848.64 | 0.963 | 0.06 | 0.059 |
# parameters of interest
params_lcs_imagi_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_imagi_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ sb07_15_t1", "sb07_15_t1 ~~ sb07_15_t1", "d_imagi_1 ~ sb07_15_t1", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.989 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.495 | NA | NA |
| d_imagi_1 ~1 | 0.327 | -0.165 | 0.820 | 0.840 | 1.304 | 0.192 |
| imagi_t1 ~1 | 3.932 | 3.693 | 4.172 | 5.053 | 32.208 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.142 | 0.059 | 0.224 | 0.932 | 3.375 | 0.001 |
| d_imagi_1 ~ imagi_t1 | -0.100 | -0.220 | 0.020 | -0.200 | -1.636 | 0.102 |
| d_imagi_1 ~ sb07_15_t1 | 0.036 | -0.013 | 0.085 | 0.130 | 1.442 | 0.149 |
| sb07_15_t1 ~~ sb07_15_t1 | 1.959 | 1.771 | 2.148 | 1.000 | 20.365 | 0.000 |
Results summary (frequ = frequency of skill-building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 30 | 9273.626 | 9386.393 | 0.979 | 0.042 | 0.049 |
# parameters of interest
params_lcs_imagi_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_imagi_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ frequ", "frequ ~~ frequ", "imagi_t1 ~ frequ", # frequency of skill building
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.990 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.497 | NA | NA |
| d_imagi_1 ~1 | 0.558 | 0.099 | 1.018 | 1.426 | 2.380 | 0.017 |
| imagi_t1 ~1 | 3.550 | 3.441 | 3.659 | 4.550 | 63.871 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.142 | 0.061 | 0.223 | 0.929 | 3.443 | 0.001 |
| imagi_t1 ~ frequ | 0.282 | 0.104 | 0.461 | 0.255 | 3.094 | 0.002 |
| d_imagi_1 ~ imagi_t1 | -0.133 | -0.257 | -0.009 | -0.266 | -2.108 | 0.035 |
| d_imagi_1 ~ frequ | 0.077 | -0.047 | 0.200 | 0.138 | 1.216 | 0.224 |
| frequ ~~ frequ | 0.495 | 0.335 | 0.656 | 1.000 | 6.060 | 0.000 |
Run models for all facets with a template & loop:
# create df for table:
df_table_hyp6 <- bind_rows(
#traits
params_lcs_extra_curr_specif_hyp6 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_curr_frequ_hyp6 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_curr_specif_hyp6 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_curr_frequ_hyp6 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_curr_specif_hyp6 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_curr_frequ_hyp6 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_curr_specif_hyp6 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_curr_frequ_hyp6 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_curr_specif_hyp6 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_curr_frequ_hyp6 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_curr_specif_hyp6 %>% filter(term=="d_socia_1 ~ sb07_01_t1"),
params_lcs_socia_curr_frequ_hyp6 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_curr_specif_hyp6 %>% filter(term=="d_asser_1 ~ sb07_02_t1"),
params_lcs_asser_curr_frequ_hyp6 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_curr_specif_hyp6 %>% filter(term=="d_energ_1 ~ sb07_03_t1"),
params_lcs_energ_curr_frequ_hyp6 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_curr_specif_hyp6 %>% filter(term=="d_compa_1 ~ sb07_04_t1"),
params_lcs_compa_curr_frequ_hyp6 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_curr_specif_hyp6 %>% filter(term=="d_respe_1 ~ sb07_05_t1"),
params_lcs_respe_curr_frequ_hyp6 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_curr_specif_hyp6 %>% filter(term=="d_trust_1 ~ sb07_06_t1"),
params_lcs_trust_curr_frequ_hyp6 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_curr_specif_hyp6 %>% filter(term=="d_organ_1 ~ sb07_07_t1"),
params_lcs_organ_curr_frequ_hyp6 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_curr_specif_hyp6 %>% filter(term=="d_produ_1 ~ sb07_08_t1"),
params_lcs_produ_curr_frequ_hyp6 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_curr_specif_hyp6 %>% filter(term=="d_respo_1 ~ sb07_09_t1"),
params_lcs_respo_curr_frequ_hyp6 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_curr_specif_hyp6 %>% filter(term=="d_anxie_1 ~ sb07_10_t1"),
params_lcs_anxie_curr_frequ_hyp6 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_curr_specif_hyp6 %>% filter(term=="d_depre_1 ~ sb07_11_t1"),
params_lcs_depre_curr_frequ_hyp6 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_curr_specif_hyp6 %>% filter(term=="d_volat_1 ~ sb07_12_t1"),
params_lcs_volat_curr_frequ_hyp6 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_curr_specif_hyp6 %>% filter(term=="d_curio_1 ~ sb07_13_t1"),
params_lcs_curio_curr_frequ_hyp6 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_curr_specif_hyp6 %>% filter(term=="d_aesth_1 ~ sb07_14_t1"),
params_lcs_aesth_curr_frequ_hyp6 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_curr_specif_hyp6 %>% filter(term=="d_imagi_1 ~ sb07_15_t1"),
params_lcs_imagi_curr_frequ_hyp6 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, estimate, std.all, statistic, p.value)Results summary across the Big Five traits: trait-specific change goals (goals) and frequency of skill-building behaviors (frequency) as moderators on the latent change score
kable(df_table_hyp6[1:10, ], digits = 3)| trait | moderator | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| extraversion | goals | 0.070 | 0.138 | 0.939 | 0.348 |
| extraversion | frequency | 0.083 | 0.177 | 1.862 | 0.063 |
| agreeableness | goals | 0.018 | 0.072 | 0.661 | 0.509 |
| agreeableness | frequency | 0.088 | 0.290 | 2.771 | 0.006 |
| conscientiousness | goals | 0.028 | 0.105 | 0.949 | 0.343 |
| conscientiousness | frequency | 0.093 | 0.206 | 2.460 | 0.014 |
| neuroticism | goals | 0.071 | 0.166 | 1.253 | 0.210 |
| neuroticism | frequency | -0.069 | -0.099 | -1.203 | 0.229 |
| openness | goals | 0.106 | 0.354 | 2.799 | 0.005 |
| openness | frequency | 0.079 | 0.291 | 2.167 | 0.030 |
Results summary across the Big Five facets: trait-specific change goals (goals) and frequency of skill-building behaviors (frequency) as moderators on the latent change score
kable(df_table_hyp6[11:40, ], digits = 3)| trait | moderator | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| sociability | goals | -0.013 | -0.030 | -0.374 | 0.708 |
| sociability | frequency | 0.055 | 0.074 | 0.813 | 0.416 |
| assertiveness | goals | 0.039 | 0.221 | 1.397 | 0.162 |
| assertiveness | frequency | 0.058 | 0.205 | 1.245 | 0.213 |
| energy | goals | 0.003 | 0.015 | 0.177 | 0.860 |
| energy | frequency | -0.066 | -0.185 | -1.556 | 0.120 |
| compassion | goals | 0.009 | 0.059 | 0.360 | 0.719 |
| compassion | frequency | 0.114 | 0.409 | 2.436 | 0.015 |
| respectfulness | goals | 0.031 | 0.130 | 1.255 | 0.209 |
| respectfulness | frequency | 0.081 | 0.183 | 1.982 | 0.047 |
| trust | goals | -0.014 | -0.055 | -0.550 | 0.583 |
| trust | frequency | -0.102 | -0.226 | -1.883 | 0.060 |
| organization | goals | -0.019 | -0.053 | -0.490 | 0.624 |
| organization | frequency | -0.122 | -0.170 | -1.907 | 0.057 |
| productiveness | goals | -0.029 | -0.107 | -1.006 | 0.314 |
| productiveness | frequency | -0.125 | -0.253 | -2.605 | 0.009 |
| responsibility | goals | 0.001 | 0.010 | 0.082 | 0.935 |
| responsibility | frequency | 0.032 | 0.113 | 0.951 | 0.342 |
| anxiety | goals | -0.056 | -0.153 | -1.351 | 0.177 |
| anxiety | frequency | 0.068 | 0.086 | 0.883 | 0.377 |
| depression | goals | -0.022 | -0.095 | -0.741 | 0.459 |
| depression | frequency | 0.021 | 0.040 | 0.444 | 0.657 |
| volatility | goals | -0.028 | -0.073 | -0.824 | 0.410 |
| volatility | frequency | -0.067 | -0.096 | -1.033 | 0.302 |
| curiosity | goals | 0.005 | 0.040 | 0.201 | 0.841 |
| curiosity | frequency | 0.095 | 0.392 | 1.846 | 0.065 |
| aesthetic | goals | 0.003 | 0.155 | 1.885 | 0.059 |
| aesthetic | frequency | 0.006 | 0.180 | 1.948 | 0.051 |
| imagination | goals | 0.036 | 0.130 | 1.442 | 0.149 |
| imagination | frequency | 0.077 | 0.138 | 1.216 | 0.224 |
Prepare data frame for plotting:
df_table_hyp6_plot <- bind_rows(
#traits
params_lcs_extra_curr_specif_hyp6 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_curr_frequ_hyp6 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_curr_specif_hyp6 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_curr_frequ_hyp6 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_curr_specif_hyp6 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_curr_frequ_hyp6 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_curr_specif_hyp6 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_curr_frequ_hyp6 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_curr_specif_hyp6 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_curr_frequ_hyp6 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_curr_specif_hyp6 %>% filter(term=="d_socia_1 ~ sb07_01_t1"),
params_lcs_socia_curr_frequ_hyp6 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_curr_specif_hyp6 %>% filter(term=="d_asser_1 ~ sb07_02_t1"),
params_lcs_asser_curr_frequ_hyp6 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_curr_specif_hyp6 %>% filter(term=="d_energ_1 ~ sb07_03_t1"),
params_lcs_energ_curr_frequ_hyp6 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_curr_specif_hyp6 %>% filter(term=="d_compa_1 ~ sb07_04_t1"),
params_lcs_compa_curr_frequ_hyp6 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_curr_specif_hyp6 %>% filter(term=="d_respe_1 ~ sb07_05_t1"),
params_lcs_respe_curr_frequ_hyp6 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_curr_specif_hyp6 %>% filter(term=="d_trust_1 ~ sb07_06_t1"),
params_lcs_trust_curr_frequ_hyp6 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_curr_specif_hyp6 %>% filter(term=="d_organ_1 ~ sb07_07_t1"),
params_lcs_organ_curr_frequ_hyp6 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_curr_specif_hyp6 %>% filter(term=="d_produ_1 ~ sb07_08_t1"),
params_lcs_produ_curr_frequ_hyp6 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_curr_specif_hyp6 %>% filter(term=="d_respo_1 ~ sb07_09_t1"),
params_lcs_respo_curr_frequ_hyp6 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_curr_specif_hyp6 %>% filter(term=="d_anxie_1 ~ sb07_10_t1"),
params_lcs_anxie_curr_frequ_hyp6 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_curr_specif_hyp6 %>% filter(term=="d_depre_1 ~ sb07_11_t1"),
params_lcs_depre_curr_frequ_hyp6 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_curr_specif_hyp6 %>% filter(term=="d_volat_1 ~ sb07_12_t1"),
params_lcs_volat_curr_frequ_hyp6 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_curr_specif_hyp6 %>% filter(term=="d_curio_1 ~ sb07_13_t1"),
params_lcs_curio_curr_frequ_hyp6 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_curr_specif_hyp6 %>% filter(term=="d_aesth_1 ~ sb07_14_t1"),
params_lcs_aesth_curr_frequ_hyp6 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_curr_specif_hyp6 %>% filter(term=="d_imagi_1 ~ sb07_15_t1"),
params_lcs_imagi_curr_frequ_hyp6 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp6_plot <- df_table_hyp6_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor("current", levels = c("current"), labels = c("current"))) %>%
mutate(moderator = factor(moderator, levels = c("goals", "frequency"), labels = c("goals", "frequency"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_hyp6_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + moderator, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
Desire to change and frequency of self-acceptance behaviors measured at the follow-up assessment will be positively related to change in ideal-self ratings in the self-acceptance group.
To test this hypothesis, we will estimate the mean-level difference in ideal trait ratings between baseline and follow up using a latent change model for each big five domain and facet. We will then include two moderators. The first will indicate how much the individual wanted to accept themselves on a given big five domain or facet. The second will indicate their frequency of self-acceptance behaviors. We will estimate the main effects of each of these variables and the interaction between these variables on the trait change score.
Reshape and split data set by intervention group:
df_sbsa_wide_pers_sa_mod <- df_sbsa %>%
filter(rando=="Self-Acceptance") %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sa07", # facet-specific acceptance goals
"sa04"))) %>% # frequency self-acceptance behaviors
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with(c("sa07", "sa04")))) %>%
select(-c(sa07_01_t2, sa07_02_t2, sa07_03_t2, sa07_04_t2, sa07_05_t2,
sa07_06_t2, sa07_07_t2, sa07_08_t2, sa07_09_t2, sa07_10_t2,
sa07_11_t2, sa07_12_t2, sa07_13_t2, sa07_14_t2, sa07_15_t2,
sa04_01_t1, sa04_02_t1, sa04_03_t1)) # frequency of self-acceptance behaviors measured at T2
# colnames(df_sbsa_wide_pers_sa_mod)
group_assign <- df_sbsa %>% select(pid, rando) %>% unique()
df_sbsa_wide_pers_sa_mod <- df_sbsa_wide_pers %>% left_join(group_assign) %>% filter(rando=="Self-Acceptance") %>% select(-rando) %>%
left_join(df_sbsa_wide_pers_sa_mod)Run models for all traits with a template & loop:
# create templates:
# 1st, for facet-specific acceptance goals
trait_template_mod_goal_accept <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadings
goals =~ 1*ind_goal_1 + ind_goal_2 + ind_goal_3 # latent variable for moderator
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~ goals # This estimates the moderation effect on personality at T1
d_trait_1 ~ trait_t1 + goals # This estimates the self-feedback parameter and the moderation effect on the change score
goals ~ 0*1 # This fixes the intercept of the moderator to 0
goals ~~ goals # This estimates the variance of the moderator
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind_goal_1 ~~ ind_goal_1
ind_goal_2 ~~ ind_goal_2
ind_goal_3 ~~ ind_goal_3
ind_goal_1 ~ 1
ind_goal_2 ~ 1
ind_goal_3 ~ 1
'
trait_facets_nrs <- list(a1 = c(1:3), b2 = c(4:6), c3 = c(7:9), d4 = c(10:12), e5 = c(13:15)) # matching facet nrs to traits
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal (6 = ideal)
# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead!
mod_names = paste0("sa07_", str_pad(trait_facets_nrs[[i]], 2, pad = "0"), "_t1")
template_filled <- str_replace_all(trait_template_mod_goal_accept,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"),
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3"),
"ind_goal_1" = mod_names[1], "ind_goal_2" = mod_names[2], "ind_goal_3" = mod_names[3]))
trait_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), trait_model_fit))
}
# 2nd, for frequency of self-acceptance behavior
trait_template_mod_frequ_accept <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for extra_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
frequ =~ 1*sa04_01_t2 + sa04_02_t2 + sa04_03_t2 # latent variable for moderator
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~ frequ # This estimates the moderation effect on personality at T1
d_trait_1 ~ trait_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa04_01_t2 ~~ sa04_01_t2
sa04_02_t2 ~~ sa04_02_t2
sa04_03_t2 ~~ sa04_03_t2
sa04_01_t2 ~ 1
sa04_02_t2 ~ 1
sa04_03_t2 ~ 1
'
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_mod_frequ_accept,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"),
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), trait_model_fit))
} Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 26 | 5704.159 | 5800.63 | 0.954 | 0.059 | 0.048 |
# parameters of interest
params_lcs_extra_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_extra_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ goals", "goals ~~ goals", "extra_t1 ~ goals", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.049 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.786 | NA | NA |
| d_extra_1 ~1 | 1.212 | 0.686 | 1.737 | 4.651 | 4.514 | 0.000 |
| extra_t1 ~1 | 3.843 | 3.777 | 3.909 | 11.052 | 114.075 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.053 | 0.023 | 0.083 | 0.783 | 3.490 | 0.000 |
| extra_t1 ~ goals | 0.012 | -0.069 | 0.092 | 0.028 | 0.287 | 0.774 |
| d_extra_1 ~ extra_t1 | -0.323 | -0.459 | -0.187 | -0.431 | -4.657 | 0.000 |
| d_extra_1 ~ goals | -0.051 | -0.118 | 0.015 | -0.164 | -1.515 | 0.130 |
| goals ~~ goals | 0.696 | 0.321 | 1.071 | 1.000 | 3.641 | 0.000 |
Results summary (frequ = frequency of skill building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 26 | 5318.86 | 5415.331 | 1 | 0 | 0.033 |
# parameters of interest
params_lcs_extra_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_extra_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ frequ", "frequ ~~ frequ", "extra_t1 ~ frequ", # frequency of skill building
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.046 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.786 | NA | NA |
| d_extra_1 ~1 | 1.294 | 0.731 | 1.857 | 4.895 | 4.504 | 0.000 |
| extra_t1 ~1 | 3.843 | 3.777 | 3.909 | 10.922 | 113.877 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.056 | 0.027 | 0.085 | 0.801 | 3.798 | 0.000 |
| extra_t1 ~ frequ | 0.108 | 0.035 | 0.181 | 0.237 | 2.904 | 0.004 |
| d_extra_1 ~ extra_t1 | -0.344 | -0.490 | -0.199 | -0.459 | -4.635 | 0.000 |
| d_extra_1 ~ frequ | 0.037 | -0.025 | 0.098 | 0.108 | 1.176 | 0.240 |
| frequ ~~ frequ | 0.594 | 0.407 | 0.781 | 1.000 | 6.216 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 26 | 5767.942 | 5864.413 | 1 | 0 | 0.03 |
# parameters of interest
params_lcs_agree_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_agree_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ goals", "goals ~~ goals", "agree_t1 ~ goals", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 0.954 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.617 | NA | NA |
| d_agree_1 ~1 | 0.596 | 0.181 | 1.011 | 2.020 | 2.813 | 0.005 |
| agree_t1 ~1 | 3.936 | 3.861 | 4.011 | 8.625 | 103.268 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.081 | 0.044 | 0.119 | 0.936 | 4.297 | 0.000 |
| agree_t1 ~ goals | 0.000 | -0.089 | 0.089 | 0.000 | -0.002 | 0.998 |
| d_agree_1 ~ agree_t1 | -0.160 | -0.262 | -0.057 | -0.247 | -3.050 | 0.002 |
| d_agree_1 ~ goals | -0.020 | -0.093 | 0.052 | -0.055 | -0.549 | 0.583 |
| goals ~~ goals | 0.633 | 0.332 | 0.934 | 1.000 | 4.122 | 0.000 |
Results summary (frequ = frequency of skill building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 26 | 5514.666 | 5611.137 | 1 | 0 | 0.032 |
# parameters of interest
params_lcs_agree_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_agree_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ frequ", "frequ ~~ frequ", "agree_t1 ~ frequ", # frequency of skill building
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 0.953 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.617 | NA | NA |
| d_agree_1 ~1 | 0.703 | 0.225 | 1.181 | 2.403 | 2.882 | 0.004 |
| agree_t1 ~1 | 3.936 | 3.861 | 4.011 | 8.709 | 103.353 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.079 | 0.044 | 0.114 | 0.922 | 4.367 | 0.000 |
| agree_t1 ~ frequ | 0.178 | 0.093 | 0.263 | 0.306 | 4.084 | 0.000 |
| d_agree_1 ~ agree_t1 | -0.187 | -0.306 | -0.068 | -0.289 | -3.088 | 0.002 |
| d_agree_1 ~ frequ | 0.053 | -0.024 | 0.131 | 0.141 | 1.345 | 0.178 |
| frequ ~~ frequ | 0.602 | 0.419 | 0.786 | 1.000 | 6.435 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 26 | 5207.128 | 5303.599 | 0.986 | 0.041 | 0.036 |
# parameters of interest
params_lcs_consc_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_consc_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ goals", "goals ~~ goals", "consc_t1 ~ goals", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.747 | NA | NA |
| d_consc_1 ~1 | 1.240 | 0.509 | 1.970 | 3.986 | 3.327 | 0.001 |
| consc_t1 ~1 | 4.392 | 4.335 | 4.449 | 10.755 | 151.561 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.084 | 0.054 | 0.114 | 0.865 | 5.431 | 0.000 |
| consc_t1 ~ goals | -0.040 | -0.091 | 0.011 | -0.112 | -1.526 | 0.127 |
| d_consc_1 ~ consc_t1 | -0.278 | -0.440 | -0.116 | -0.365 | -3.371 | 0.001 |
| d_consc_1 ~ goals | -0.026 | -0.067 | 0.016 | -0.096 | -1.218 | 0.223 |
| goals ~~ goals | 1.323 | 0.991 | 1.656 | 1.000 | 7.801 | 0.000 |
Results summary (frequ = frequency of skill building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 26 | 4900.903 | 4997.374 | 0.991 | 0.032 | 0.036 |
# parameters of interest
params_lcs_consc_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_consc_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ frequ", "frequ ~~ frequ", "consc_t1 ~ frequ", # frequency of skill building
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.979 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.743 | NA | NA |
| d_consc_1 ~1 | 1.352 | 0.548 | 2.156 | 4.317 | 3.296 | 0.001 |
| consc_t1 ~1 | 4.392 | 4.335 | 4.449 | 10.640 | 151.452 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.084 | 0.054 | 0.113 | 0.854 | 5.543 | 0.000 |
| consc_t1 ~ frequ | 0.167 | 0.079 | 0.255 | 0.309 | 3.707 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.304 | -0.482 | -0.125 | -0.400 | -3.333 | 0.001 |
| d_consc_1 ~ frequ | 0.064 | -0.015 | 0.143 | 0.157 | 1.595 | 0.111 |
| frequ ~~ frequ | 0.583 | 0.396 | 0.770 | 1.000 | 6.112 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 26 | 5529.444 | 5625.915 | 0.964 | 0.056 | 0.048 |
# parameters of interest
params_lcs_neuro_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_neuro_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ goals", "goals ~~ goals", "neuro_t1 ~ goals", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.897 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.771 | NA | NA |
| d_neuro_1 ~1 | 0.423 | 0.148 | 0.697 | 1.296 | 3.016 | 0.003 |
| neuro_t1 ~1 | 1.657 | 1.602 | 1.713 | 4.369 | 58.301 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.094 | 0.056 | 0.133 | 0.885 | 4.808 | 0.000 |
| neuro_t1 ~ goals | -0.006 | -0.070 | 0.057 | -0.013 | -0.193 | 0.847 |
| d_neuro_1 ~ neuro_t1 | -0.246 | -0.418 | -0.074 | -0.286 | -2.804 | 0.005 |
| d_neuro_1 ~ goals | 0.073 | -0.014 | 0.160 | 0.178 | 1.649 | 0.099 |
| goals ~~ goals | 0.634 | 0.146 | 1.121 | 1.000 | 2.549 | 0.011 |
Results summary (frequ = frequency of skill building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 26 | 5207.826 | 5304.297 | 0.997 | 0.016 | 0.034 |
# parameters of interest
params_lcs_neuro_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_neuro_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ frequ", "frequ ~~ frequ", "neuro_t1 ~ frequ", # frequency of skill building
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.898 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.771 | NA | NA |
| d_neuro_1 ~1 | 0.456 | 0.170 | 0.742 | 1.415 | 3.125 | 0.002 |
| neuro_t1 ~1 | 1.658 | 1.602 | 1.714 | 4.414 | 58.286 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.094 | 0.057 | 0.131 | 0.903 | 4.930 | 0.000 |
| neuro_t1 ~ frequ | -0.087 | -0.166 | -0.009 | -0.180 | -2.177 | 0.029 |
| d_neuro_1 ~ neuro_t1 | -0.267 | -0.446 | -0.088 | -0.311 | -2.918 | 0.004 |
| d_neuro_1 ~ frequ | -0.049 | -0.123 | 0.024 | -0.118 | -1.321 | 0.187 |
| frequ ~~ frequ | 0.598 | 0.408 | 0.788 | 1.000 | 6.180 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 26 | 5531.677 | 5628.148 | 1 | 0 | 0.028 |
# parameters of interest
params_lcs_openn_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_openn_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ goals", "goals ~~ goals", "openn_t1 ~ goals", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.954 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.669 | NA | NA |
| d_openn_1 ~1 | 0.743 | 0.192 | 1.293 | 2.660 | 2.643 | 0.008 |
| openn_t1 ~1 | 3.897 | 3.831 | 3.963 | 9.796 | 115.380 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.072 | 0.039 | 0.104 | 0.921 | 4.328 | 0.000 |
| openn_t1 ~ goals | -0.082 | -0.165 | 0.001 | -0.148 | -1.932 | 0.053 |
| d_openn_1 ~ openn_t1 | -0.197 | -0.333 | -0.060 | -0.280 | -2.820 | 0.005 |
| d_openn_1 ~ goals | 0.000 | -0.067 | 0.067 | 0.000 | -0.001 | 1.000 |
| goals ~~ goals | 0.519 | 0.293 | 0.746 | 1.000 | 4.499 | 0.000 |
Results summary (frequ = frequency of skill building behavior):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 26 | 5373.969 | 5470.44 | 1 | 0 | 0.03 |
# parameters of interest
params_lcs_openn_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_openn_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ frequ", "frequ ~~ frequ", "openn_t1 ~ frequ", # frequency of skill building
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.953 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.669 | NA | NA |
| d_openn_1 ~1 | 0.788 | 0.210 | 1.366 | 2.832 | 2.673 | 0.008 |
| openn_t1 ~1 | 3.897 | 3.831 | 3.963 | 9.829 | 115.434 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.071 | 0.039 | 0.103 | 0.918 | 4.357 | 0.000 |
| openn_t1 ~ frequ | 0.142 | 0.062 | 0.222 | 0.281 | 3.465 | 0.001 |
| d_openn_1 ~ openn_t1 | -0.208 | -0.352 | -0.064 | -0.297 | -2.838 | 0.005 |
| d_openn_1 ~ frequ | 0.022 | -0.046 | 0.089 | 0.061 | 0.628 | 0.530 |
| frequ ~~ frequ | 0.617 | 0.429 | 0.804 | 1.000 | 6.447 | 0.000 |
Run models for all facets with a template & loop:
# create templates:
# 1st, for facet-specific acceptance goal
facet_template_mod_goal_accept <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~ ind_goal # This estimates the moderation effect on personality at T1
d_facet_1 ~ facet_t1 + ind_goal # This estimates the self-feedback parameter and the moderation effect on the change score
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
items = paste0(bfi_versions[[6]], item_nrs)
mod_name = paste0("sa07_", str_pad(i-5, 2, pad = "0"), "_t1")
template_filled <- str_replace_all(facet_template_mod_goal_accept,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = mod_name))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), facet_model_fit))
}
# 2nd, for frequency of self-acceptance behavior
facet_template_mod_frequ_accept <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
frequ =~ 1*sa04_01_t2 + sa04_02_t2 + sa04_03_t2 # latent variable for moderator
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~ frequ # This estimates the moderation effect on personality at T1
d_facet_1 ~ facet_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
sa04_01_t2 ~~ sa04_01_t2
sa04_02_t2 ~~ sa04_02_t2
sa04_03_t2 ~~ sa04_03_t2
sa04_01_t2 ~ 1
sa04_02_t2 ~ 1
sa04_03_t2 ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
items = paste0(bfi_versions[[6]], item_nrs)
template_filled <- str_replace_all(facet_template_mod_frequ_accept,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), facet_model_fit))
} Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7401.301 | 7486.641 | 0.929 | 0.062 | 0.056 |
# parameters of interest
params_lcs_socia_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_socia_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ sa07_01_t1", "sa07_01_t1 ~~ sa07_01_t1", "d_socia_1 ~ sa07_01_t1", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.998 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.691 | NA | NA |
| d_socia_1 ~1 | 1.012 | 0.252 | 1.772 | 3.846 | 2.611 | 0.009 |
| socia_t1 ~1 | 4.185 | 3.994 | 4.375 | 11.002 | 43.117 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.059 | 0.009 | 0.110 | 0.858 | 2.312 | 0.021 |
| d_socia_1 ~ socia_t1 | -0.222 | -0.412 | -0.032 | -0.321 | -2.291 | 0.022 |
| d_socia_1 ~ sa07_01_t1 | -0.030 | -0.079 | 0.019 | -0.156 | -1.195 | 0.232 |
| sa07_01_t1 ~~ sa07_01_t1 | 1.866 | 1.657 | 2.075 | 1.000 | 17.476 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 9018.396 | 9129.708 | 0.959 | 0.045 | 0.053 |
# parameters of interest
params_lcs_socia_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_socia_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ frequ", "frequ ~~ frequ", "socia_t1 ~ frequ", # frequency of skill building
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.001 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.701 | NA | NA |
| d_socia_1 ~1 | 1.020 | 0.196 | 1.844 | 3.950 | 2.427 | 0.015 |
| socia_t1 ~1 | 4.318 | 4.241 | 4.394 | 11.707 | 110.092 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.058 | 0.008 | 0.109 | 0.876 | 2.258 | 0.024 |
| socia_t1 ~ frequ | 0.002 | -0.083 | 0.086 | 0.004 | 0.039 | 0.969 |
| d_socia_1 ~ socia_t1 | -0.246 | -0.434 | -0.058 | -0.352 | -2.569 | 0.010 |
| d_socia_1 ~ frequ | -0.002 | -0.072 | 0.068 | -0.005 | -0.051 | 0.959 |
| frequ ~~ frequ | 0.600 | 0.411 | 0.789 | 1.000 | 6.219 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7619.02 | 7704.36 | 0.971 | 0.037 | 0.046 |
# parameters of interest
params_lcs_asser_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_asser_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ sa07_02_t1", "sa07_02_t1 ~~ sa07_02_t1", "d_asser_1 ~ sa07_02_t1", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.012 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.720 | NA | NA |
| d_asser_1 ~1 | 1.090 | 0.324 | 1.857 | 5.428 | 2.788 | 0.005 |
| asser_t1 ~1 | 4.088 | 3.944 | 4.231 | 14.487 | 55.901 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.034 | 0.003 | 0.066 | 0.852 | 2.155 | 0.031 |
| d_asser_1 ~ asser_t1 | -0.258 | -0.443 | -0.074 | -0.363 | -2.748 | 0.006 |
| d_asser_1 ~ sa07_02_t1 | -0.014 | -0.040 | 0.012 | -0.098 | -1.083 | 0.279 |
| sa07_02_t1 ~~ sa07_02_t1 | 1.876 | 1.675 | 2.076 | 1.000 | 18.353 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 9215.153 | 9326.466 | 0.993 | 0.019 | 0.041 |
# parameters of interest
params_lcs_asser_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_asser_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ frequ", "frequ ~~ frequ", "asser_t1 ~ frequ", # frequency of skill building
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.014 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.722 | NA | NA |
| d_asser_1 ~1 | 1.234 | 0.437 | 2.030 | 6.000 | 3.034 | 0.002 |
| asser_t1 ~1 | 4.148 | 4.052 | 4.244 | 14.368 | 84.467 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.035 | 0.004 | 0.065 | 0.818 | 2.212 | 0.027 |
| asser_t1 ~ frequ | 0.093 | 0.011 | 0.174 | 0.246 | 2.231 | 0.026 |
| d_asser_1 ~ asser_t1 | -0.304 | -0.493 | -0.114 | -0.427 | -3.141 | 0.002 |
| d_asser_1 ~ frequ | 0.056 | -0.006 | 0.118 | 0.208 | 1.769 | 0.077 |
| frequ ~~ frequ | 0.585 | 0.400 | 0.770 | 1.000 | 6.186 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 6953.933 | 7039.273 | 0.953 | 0.038 | 0.048 |
# parameters of interest
params_lcs_energ_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_energ_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ sa07_03_t1", "sa07_03_t1 ~~ sa07_03_t1", "d_energ_1 ~ sa07_03_t1", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.089 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.745 | NA | NA |
| d_energ_1 ~1 | 0.701 | 0.242 | 1.160 | 6.455 | 2.991 | 0.003 |
| energ_t1 ~1 | 2.433 | 2.302 | 2.564 | 15.333 | 36.491 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.009 | -0.014 | 0.032 | 0.778 | 0.774 | 0.439 |
| d_energ_1 ~ energ_t1 | -0.305 | -0.497 | -0.112 | -0.445 | -3.099 | 0.002 |
| d_energ_1 ~ sa07_03_t1 | 0.009 | -0.010 | 0.028 | 0.116 | 0.908 | 0.364 |
| sa07_03_t1 ~~ sa07_03_t1 | 1.949 | 1.744 | 2.153 | 1.000 | 18.643 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 8529.092 | 8640.404 | 0.967 | 0.035 | 0.051 |
# parameters of interest
params_lcs_energ_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_energ_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ frequ", "frequ ~~ frequ", "energ_t1 ~ frequ", # frequency of skill building
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.060 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.743 | NA | NA |
| d_energ_1 ~1 | 0.817 | 0.283 | 1.351 | 7.029 | 2.998 | 0.003 |
| energ_t1 ~1 | 2.395 | 2.273 | 2.516 | 14.453 | 38.632 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.011 | -0.013 | 0.034 | 0.798 | 0.907 | 0.364 |
| energ_t1 ~ frequ | -0.084 | -0.173 | 0.005 | -0.390 | -1.855 | 0.064 |
| d_energ_1 ~ energ_t1 | -0.341 | -0.565 | -0.117 | -0.486 | -2.981 | 0.003 |
| d_energ_1 ~ frequ | -0.022 | -0.070 | 0.026 | -0.147 | -0.902 | 0.367 |
| frequ ~~ frequ | 0.592 | 0.410 | 0.774 | 1.000 | 6.368 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7874.185 | 7959.525 | 1 | 0 | 0.033 |
# parameters of interest
params_lcs_compa_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_compa_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ sa07_04_t1", "sa07_04_t1 ~~ sa07_04_t1", "d_compa_1 ~ sa07_04_t1", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 0.941 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.569 | NA | NA |
| d_compa_1 ~1 | 0.529 | -0.450 | 1.508 | 1.482 | 1.059 | 0.290 |
| compa_t1 ~1 | 4.306 | 4.133 | 4.478 | 7.300 | 48.964 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.120 | 0.002 | 0.238 | 0.943 | 1.990 | 0.047 |
| d_compa_1 ~ compa_t1 | -0.119 | -0.334 | 0.095 | -0.197 | -1.093 | 0.274 |
| d_compa_1 ~ sa07_04_t1 | -0.037 | -0.097 | 0.022 | -0.138 | -1.238 | 0.216 |
| sa07_04_t1 ~~ sa07_04_t1 | 1.729 | 1.536 | 1.923 | 1.000 | 17.554 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 9488.092 | 9599.405 | 0.98 | 0.03 | 0.052 |
# parameters of interest
params_lcs_compa_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_compa_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ frequ", "frequ ~~ frequ", "compa_t1 ~ frequ", # frequency of skill building
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 0.938 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.571 | NA | NA |
| d_compa_1 ~1 | 0.356 | -0.726 | 1.438 | 0.973 | 0.644 | 0.519 |
| compa_t1 ~1 | 4.293 | 4.199 | 4.388 | 7.145 | 89.151 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.128 | -0.001 | 0.258 | 0.958 | 1.940 | 0.052 |
| compa_t1 ~ frequ | 0.293 | 0.163 | 0.423 | 0.375 | 4.404 | 0.000 |
| d_compa_1 ~ compa_t1 | -0.100 | -0.345 | 0.145 | -0.165 | -0.802 | 0.423 |
| d_compa_1 ~ frequ | -0.036 | -0.193 | 0.121 | -0.075 | -0.447 | 0.655 |
| frequ ~~ frequ | 0.593 | 0.411 | 0.775 | 1.000 | 6.383 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 6317.585 | 6402.924 | 0.982 | 0.031 | 0.045 |
# parameters of interest
params_lcs_respe_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_respe_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ sa07_05_t1", "sa07_05_t1 ~~ sa07_05_t1", "d_respe_1 ~ sa07_05_t1", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.871 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.564 | NA | NA |
| d_respe_1 ~1 | 0.262 | -0.600 | 1.123 | 1.308 | 0.595 | 0.552 |
| respe_t1 ~1 | 4.760 | 4.679 | 4.841 | 15.411 | 115.183 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.040 | 0.010 | 0.069 | 0.991 | 2.651 | 0.008 |
| d_respe_1 ~ respe_t1 | -0.055 | -0.233 | 0.122 | -0.085 | -0.610 | 0.542 |
| d_respe_1 ~ sa07_05_t1 | -0.008 | -0.042 | 0.026 | -0.052 | -0.470 | 0.639 |
| sa07_05_t1 ~~ sa07_05_t1 | 1.673 | 1.379 | 1.968 | 1.000 | 11.142 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 7934.6 | 8045.913 | 0.987 | 0.026 | 0.041 |
# parameters of interest
params_lcs_respe_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_respe_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ frequ", "frequ ~~ frequ", "respe_t1 ~ frequ", # frequency of skill building
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.870 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.564 | NA | NA |
| d_respe_1 ~1 | 0.347 | -0.623 | 1.316 | 1.717 | 0.701 | 0.484 |
| respe_t1 ~1 | 4.703 | 4.645 | 4.760 | 15.095 | 161.204 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.040 | 0.011 | 0.069 | 0.985 | 2.676 | 0.007 |
| respe_t1 ~ frequ | 0.160 | 0.092 | 0.228 | 0.393 | 4.641 | 0.000 |
| d_respe_1 ~ respe_t1 | -0.077 | -0.280 | 0.127 | -0.118 | -0.738 | 0.461 |
| d_respe_1 ~ frequ | 0.028 | -0.042 | 0.098 | 0.106 | 0.781 | 0.435 |
| frequ ~~ frequ | 0.585 | 0.404 | 0.766 | 1.000 | 6.329 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7651.257 | 7736.597 | 0.984 | 0.029 | 0.039 |
# parameters of interest
params_lcs_trust_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_trust_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ sa07_06_t1", "sa07_06_t1 ~~ sa07_06_t1", "d_trust_1 ~ sa07_06_t1", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.981 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.659 | NA | NA |
| d_trust_1 ~1 | 0.347 | 0.009 | 0.684 | 1.287 | 2.014 | 0.044 |
| trust_t1 ~1 | 1.993 | 1.852 | 2.133 | 4.963 | 27.855 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.065 | 0.019 | 0.111 | 0.890 | 2.754 | 0.006 |
| d_trust_1 ~ trust_t1 | -0.196 | -0.355 | -0.037 | -0.293 | -2.418 | 0.016 |
| d_trust_1 ~ sa07_06_t1 | 0.024 | -0.012 | 0.060 | 0.125 | 1.298 | 0.194 |
| sa07_06_t1 ~~ sa07_06_t1 | 1.987 | 1.779 | 2.196 | 1.000 | 18.705 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 9228.741 | 9340.054 | 0.961 | 0.045 | 0.052 |
# parameters of interest
params_lcs_trust_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_trust_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ frequ", "frequ ~~ frequ", "trust_t1 ~ frequ", # frequency of skill building
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.990 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.663 | NA | NA |
| d_trust_1 ~1 | 0.511 | 0.171 | 0.851 | 1.854 | 2.945 | 0.003 |
| trust_t1 ~1 | 1.908 | 1.809 | 2.007 | 4.632 | 37.859 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.065 | 0.021 | 0.110 | 0.862 | 2.868 | 0.004 |
| trust_t1 ~ frequ | -0.147 | -0.246 | -0.047 | -0.276 | -2.876 | 0.004 |
| d_trust_1 ~ trust_t1 | -0.251 | -0.427 | -0.074 | -0.375 | -2.779 | 0.005 |
| d_trust_1 ~ frequ | -0.070 | -0.159 | 0.019 | -0.197 | -1.537 | 0.124 |
| frequ ~~ frequ | 0.603 | 0.416 | 0.789 | 1.000 | 6.335 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 6540.816 | 6626.155 | 0.937 | 0.051 | 0.048 |
# parameters of interest
params_lcs_organ_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_organ_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ sa07_07_t1", "sa07_07_t1 ~~ sa07_07_t1", "d_organ_1 ~ sa07_07_t1", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.010 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.791 | NA | NA |
| d_organ_1 ~1 | 0.518 | 0.072 | 0.963 | 2.251 | 2.277 | 0.023 |
| organ_t1 ~1 | 1.595 | 1.458 | 1.733 | 5.435 | 22.732 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.044 | 0.002 | 0.086 | 0.831 | 2.048 | 0.041 |
| d_organ_1 ~ organ_t1 | -0.326 | -0.620 | -0.031 | -0.416 | -2.165 | 0.030 |
| d_organ_1 ~ sa07_07_t1 | 0.011 | -0.018 | 0.040 | 0.073 | 0.735 | 0.462 |
| sa07_07_t1 ~~ sa07_07_t1 | 2.345 | 2.152 | 2.537 | 1.000 | 23.855 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 8053.043 | 8164.356 | 0.973 | 0.035 | 0.045 |
# parameters of interest
params_lcs_organ_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_organ_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ frequ", "frequ ~~ frequ", "organ_t1 ~ frequ", # frequency of skill building
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.020 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.802 | NA | NA |
| d_organ_1 ~1 | 0.701 | 0.220 | 1.183 | 3.371 | 2.854 | 0.004 |
| organ_t1 ~1 | 1.682 | 1.591 | 1.773 | 6.356 | 36.129 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.031 | -0.001 | 0.064 | 0.727 | 1.902 | 0.057 |
| organ_t1 ~ frequ | -0.117 | -0.187 | -0.048 | -0.335 | -3.312 | 0.001 |
| d_organ_1 ~ organ_t1 | -0.416 | -0.707 | -0.125 | -0.530 | -2.806 | 0.005 |
| d_organ_1 ~ frequ | -0.091 | -0.169 | -0.014 | -0.333 | -2.323 | 0.020 |
| frequ ~~ frequ | 0.574 | 0.394 | 0.754 | 1.000 | 6.244 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 6115.106 | 6200.445 | 0.862 | 0.07 | 0.065 |
# parameters of interest
params_lcs_produ_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_produ_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ sa07_08_t1", "sa07_08_t1 ~~ sa07_08_t1", "d_produ_1 ~ sa07_08_t1", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.959 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.634 | NA | NA |
| d_produ_1 ~1 | 0.272 | -0.137 | 0.681 | 1.316 | 1.304 | 0.192 |
| produ_t1 ~1 | 1.546 | 1.429 | 1.663 | 4.944 | 25.804 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.040 | 0.005 | 0.075 | 0.928 | 2.223 | 0.026 |
| d_produ_1 ~ produ_t1 | -0.173 | -0.441 | 0.095 | -0.262 | -1.267 | 0.205 |
| d_produ_1 ~ sa07_08_t1 | -0.006 | -0.032 | 0.020 | -0.043 | -0.450 | 0.653 |
| sa07_08_t1 ~~ sa07_08_t1 | 2.121 | 1.919 | 2.323 | 1.000 | 20.591 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 7673.702 | 7785.015 | 0.948 | 0.045 | 0.056 |
# parameters of interest
params_lcs_produ_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_produ_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ frequ", "frequ ~~ frequ", "produ_t1 ~ frequ", # frequency of skill building
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.954 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.631 | NA | NA |
| d_produ_1 ~1 | 0.302 | -0.114 | 0.717 | 1.476 | 1.421 | 0.155 |
| produ_t1 ~1 | 1.592 | 1.514 | 1.669 | 5.150 | 40.237 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.037 | 0.005 | 0.069 | 0.884 | 2.267 | 0.023 |
| produ_t1 ~ frequ | -0.090 | -0.159 | -0.021 | -0.226 | -2.551 | 0.011 |
| d_produ_1 ~ produ_t1 | -0.204 | -0.479 | 0.071 | -0.308 | -1.451 | 0.147 |
| d_produ_1 ~ frequ | -0.061 | -0.134 | 0.013 | -0.230 | -1.620 | 0.105 |
| frequ ~~ frequ | 0.599 | 0.410 | 0.788 | 1.000 | 6.199 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 6991.791 | 7077.131 | 0.995 | 0.015 | 0.04 |
# parameters of interest
params_lcs_respo_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_respo_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ sa07_09_t1", "sa07_09_t1 ~~ sa07_09_t1", "d_respo_1 ~ sa07_09_t1", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.983 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.724 | NA | NA |
| d_respo_1 ~1 | 1.174 | 0.379 | 1.968 | 3.260 | 2.896 | 0.004 |
| respo_t1 ~1 | 4.344 | 4.170 | 4.517 | 8.884 | 49.032 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.114 | 0.041 | 0.187 | 0.877 | 3.050 | 0.002 |
| d_respo_1 ~ respo_t1 | -0.258 | -0.431 | -0.084 | -0.350 | -2.915 | 0.004 |
| d_respo_1 ~ sa07_09_t1 | -0.017 | -0.061 | 0.028 | -0.067 | -0.728 | 0.467 |
| sa07_09_t1 ~~ sa07_09_t1 | 2.111 | 1.903 | 2.319 | 1.000 | 19.927 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 8550.712 | 8662.025 | 0.977 | 0.033 | 0.047 |
# parameters of interest
params_lcs_respo_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_respo_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ frequ", "frequ ~~ frequ", "respo_t1 ~ frequ", # frequency of skill building
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.982 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.721 | NA | NA |
| d_respo_1 ~1 | 1.218 | 0.391 | 2.045 | 3.361 | 2.887 | 0.004 |
| respo_t1 ~1 | 4.271 | 4.162 | 4.380 | 8.657 | 76.717 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.114 | 0.043 | 0.186 | 0.872 | 3.128 | 0.002 |
| respo_t1 ~ frequ | 0.213 | 0.085 | 0.340 | 0.329 | 3.278 | 0.001 |
| d_respo_1 ~ respo_t1 | -0.278 | -0.468 | -0.089 | -0.379 | -2.876 | 0.004 |
| d_respo_1 ~ frequ | 0.053 | -0.055 | 0.161 | 0.112 | 0.965 | 0.335 |
| frequ ~~ frequ | 0.583 | 0.394 | 0.771 | 1.000 | 6.049 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7620.813 | 7706.152 | 0.873 | 0.05 | 0.05 |
# parameters of interest
params_lcs_anxie_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_anxie_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ sa07_10_t1", "sa07_10_t1 ~~ sa07_10_t1", "d_anxie_1 ~ sa07_10_t1", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.813 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.873 | NA | NA |
| d_anxie_1 ~1 | 1.418 | -0.800 | 3.635 | 4.604 | 1.253 | 0.210 |
| anxie_t1 ~1 | 4.573 | 4.440 | 4.706 | 15.934 | 67.420 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.086 | -0.019 | 0.191 | 0.911 | 1.614 | 0.107 |
| d_anxie_1 ~ anxie_t1 | -0.320 | -0.789 | 0.148 | -0.299 | -1.341 | 0.180 |
| d_anxie_1 ~ sa07_10_t1 | 0.004 | -0.050 | 0.058 | 0.019 | 0.143 | 0.886 |
| sa07_10_t1 ~~ sa07_10_t1 | 2.097 | 1.899 | 2.295 | 1.000 | 20.800 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 9186.786 | 9298.099 | 0.924 | 0.048 | 0.058 |
# parameters of interest
params_lcs_anxie_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_anxie_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ frequ", "frequ ~~ frequ", "anxie_t1 ~ frequ", # frequency of skill building
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.812 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.843 | NA | NA |
| d_anxie_1 ~1 | 1.339 | -0.970 | 3.649 | 4.220 | 1.137 | 0.256 |
| anxie_t1 ~1 | 4.585 | 4.519 | 4.651 | 14.997 | 135.436 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.093 | -0.024 | 0.210 | 0.923 | 1.555 | 0.120 |
| anxie_t1 ~ frequ | 0.126 | 0.024 | 0.228 | 0.321 | 2.421 | 0.015 |
| d_anxie_1 ~ anxie_t1 | -0.302 | -0.798 | 0.195 | -0.290 | -1.190 | 0.234 |
| d_anxie_1 ~ frequ | 0.026 | -0.089 | 0.141 | 0.063 | 0.439 | 0.661 |
| frequ ~~ frequ | 0.604 | 0.415 | 0.793 | 1.000 | 6.273 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 6285.706 | 6371.046 | 0.896 | 0.067 | 0.066 |
# parameters of interest
params_lcs_depre_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_depre_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ sa07_11_t1", "sa07_11_t1 ~~ sa07_11_t1", "d_depre_1 ~ sa07_11_t1", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.921 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.538 | NA | NA |
| d_depre_1 ~1 | 0.356 | -0.958 | 1.669 | 2.650 | 0.531 | 0.596 |
| depre_t1 ~1 | 4.483 | 4.390 | 4.576 | 19.519 | 94.355 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.018 | -0.007 | 0.042 | 0.981 | 1.401 | 0.161 |
| d_depre_1 ~ depre_t1 | -0.080 | -0.367 | 0.206 | -0.138 | -0.551 | 0.582 |
| d_depre_1 ~ sa07_11_t1 | 0.001 | -0.022 | 0.024 | 0.008 | 0.062 | 0.950 |
| sa07_11_t1 ~~ sa07_11_t1 | 2.223 | 2.029 | 2.417 | 1.000 | 22.481 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 7847.38 | 7958.693 | 0.91 | 0.064 | 0.067 |
# parameters of interest
params_lcs_depre_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_depre_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ frequ", "frequ ~~ frequ", "depre_t1 ~ frequ", # frequency of skill building
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.918 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.540 | NA | NA |
| d_depre_1 ~1 | 0.384 | -0.847 | 1.615 | 2.777 | 0.612 | 0.541 |
| depre_t1 ~1 | 4.410 | 4.345 | 4.476 | 18.738 | 132.496 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.018 | -0.008 | 0.044 | 0.955 | 1.384 | 0.166 |
| depre_t1 ~ frequ | 0.022 | -0.035 | 0.079 | 0.072 | 0.749 | 0.454 |
| d_depre_1 ~ depre_t1 | -0.086 | -0.362 | 0.189 | -0.147 | -0.614 | 0.539 |
| d_depre_1 ~ frequ | 0.029 | -0.017 | 0.076 | 0.165 | 1.244 | 0.214 |
| frequ ~~ frequ | 0.606 | 0.413 | 0.798 | 1.000 | 6.165 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7075.037 | 7160.377 | 0.931 | 0.049 | 0.051 |
# parameters of interest
params_lcs_volat_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_volat_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ sa07_12_t1", "sa07_12_t1 ~~ sa07_12_t1", "d_volat_1 ~ sa07_12_t1", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.834 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.691 | NA | NA |
| d_volat_1 ~1 | 0.274 | -0.196 | 0.743 | 0.782 | 1.143 | 0.253 |
| volat_t1 ~1 | 1.701 | 1.528 | 1.873 | 4.027 | 19.331 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.120 | 0.029 | 0.210 | 0.977 | 2.591 | 0.010 |
| d_volat_1 ~ volat_t1 | -0.124 | -0.387 | 0.138 | -0.150 | -0.928 | 0.353 |
| d_volat_1 ~ sa07_12_t1 | -0.008 | -0.059 | 0.043 | -0.029 | -0.305 | 0.761 |
| sa07_12_t1 ~~ sa07_12_t1 | 1.655 | 1.466 | 1.843 | 1.000 | 17.206 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 8709.639 | 8820.952 | 0.964 | 0.038 | 0.052 |
# parameters of interest
params_lcs_volat_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_volat_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ frequ", "frequ ~~ frequ", "volat_t1 ~ frequ", # frequency of skill building
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.841 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.683 | NA | NA |
| d_volat_1 ~1 | 0.302 | -0.135 | 0.738 | 0.936 | 1.354 | 0.176 |
| volat_t1 ~1 | 1.686 | 1.604 | 1.769 | 4.247 | 40.193 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.099 | 0.023 | 0.174 | 0.949 | 2.548 | 0.011 |
| volat_t1 ~ frequ | -0.124 | -0.213 | -0.034 | -0.237 | -2.697 | 0.007 |
| d_volat_1 ~ volat_t1 | -0.156 | -0.421 | 0.110 | -0.192 | -1.149 | 0.251 |
| d_volat_1 ~ frequ | -0.073 | -0.172 | 0.025 | -0.173 | -1.456 | 0.145 |
| frequ ~~ frequ | 0.582 | 0.394 | 0.769 | 1.000 | 6.076 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7453.397 | 7538.737 | 0.995 | 0.014 | 0.037 |
# parameters of interest
params_lcs_curio_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_curio_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ sa07_13_t1", "sa07_13_t1 ~~ sa07_13_t1", "d_curio_1 ~ sa07_13_t1", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.978 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.623 | NA | NA |
| d_curio_1 ~1 | 0.943 | -0.070 | 1.957 | 4.726 | 1.824 | 0.068 |
| curio_t1 ~1 | 4.617 | 4.508 | 4.726 | 14.731 | 82.941 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.036 | -0.007 | 0.079 | 0.899 | 1.629 | 0.103 |
| d_curio_1 ~ curio_t1 | -0.198 | -0.419 | 0.023 | -0.311 | -1.758 | 0.079 |
| d_curio_1 ~ sa07_13_t1 | -0.022 | -0.062 | 0.017 | -0.148 | -1.098 | 0.272 |
| sa07_13_t1 ~~ sa07_13_t1 | 1.786 | 1.550 | 2.023 | 1.000 | 14.799 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 9082.267 | 9193.58 | 0.981 | 0.028 | 0.047 |
# parameters of interest
params_lcs_curio_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_curio_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ frequ", "frequ ~~ frequ", "curio_t1 ~ frequ", # frequency of skill building
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.975 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.625 | NA | NA |
| d_curio_1 ~1 | 0.841 | -0.211 | 1.892 | 4.156 | 1.567 | 0.117 |
| curio_t1 ~1 | 4.518 | 4.448 | 4.588 | 14.312 | 126.371 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.038 | -0.007 | 0.082 | 0.918 | 1.648 | 0.099 |
| curio_t1 ~ frequ | 0.075 | -0.008 | 0.158 | 0.186 | 1.765 | 0.078 |
| d_curio_1 ~ curio_t1 | -0.186 | -0.416 | 0.044 | -0.291 | -1.588 | 0.112 |
| d_curio_1 ~ frequ | 0.016 | -0.061 | 0.092 | 0.061 | 0.398 | 0.691 |
| frequ ~~ frequ | 0.618 | 0.425 | 0.812 | 1.000 | 6.256 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7519.34 | 7604.68 | 0.988 | 0.032 | 0.042 |
# parameters of interest
params_lcs_aesth_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_aesth_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ sa07_14_t1", "sa07_14_t1 ~~ sa07_14_t1", "d_aesth_1 ~ sa07_14_t1", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.952 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.655 | NA | NA |
| d_aesth_1 ~1 | 0.582 | 0.122 | 1.041 | 12.860 | 2.482 | 0.013 |
| aesth_t1 ~1 | 3.152 | 3.018 | 3.287 | 47.968 | 45.956 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.002 | -0.006 | 0.010 | 0.913 | 0.440 | 0.660 |
| d_aesth_1 ~ aesth_t1 | -0.182 | -0.325 | -0.039 | -0.264 | -2.498 | 0.012 |
| d_aesth_1 ~ sa07_14_t1 | -0.004 | -0.014 | 0.006 | -0.120 | -0.781 | 0.435 |
| sa07_14_t1 ~~ sa07_14_t1 | 1.877 | 1.681 | 2.073 | 1.000 | 18.765 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 9113.488 | 9224.801 | 0.986 | 0.032 | 0.047 |
# parameters of interest
params_lcs_aesth_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_aesth_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ frequ", "frequ ~~ frequ", "aesth_t1 ~ frequ", # frequency of skill building
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.952 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.655 | NA | NA |
| d_aesth_1 ~1 | 0.677 | 0.177 | 1.177 | 15.339 | 2.656 | 0.008 |
| aesth_t1 ~1 | 3.157 | 3.020 | 3.294 | 49.219 | 45.205 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.002 | -0.006 | 0.010 | 0.894 | 0.433 | 0.665 |
| aesth_t1 ~ frequ | 0.020 | -0.025 | 0.064 | 0.235 | 0.863 | 0.388 |
| d_aesth_1 ~ aesth_t1 | -0.215 | -0.371 | -0.060 | -0.313 | -2.712 | 0.007 |
| d_aesth_1 ~ frequ | 0.011 | -0.015 | 0.037 | 0.191 | 0.826 | 0.409 |
| frequ ~~ frequ | 0.593 | 0.408 | 0.778 | 1.000 | 6.282 | 0.000 |
Results summary (*sa07_$$_t1* = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7012.38 | 7097.72 | 0.974 | 0.029 | 0.05 |
# parameters of interest
params_lcs_imagi_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_imagi_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ sa07_15_t1", "sa07_15_t1 ~~ sa07_15_t1", "d_imagi_1 ~ sa07_15_t1", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.912 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.606 | NA | NA |
| d_imagi_1 ~1 | 0.492 | -0.795 | 1.780 | 2.189 | 0.750 | 0.453 |
| imagi_t1 ~1 | 4.670 | 4.556 | 4.783 | 13.800 | 80.799 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.049 | 0.009 | 0.089 | 0.966 | 2.404 | 0.016 |
| d_imagi_1 ~ imagi_t1 | -0.117 | -0.387 | 0.154 | -0.176 | -0.846 | 0.398 |
| d_imagi_1 ~ sa07_15_t1 | 0.006 | -0.027 | 0.040 | 0.041 | 0.375 | 0.708 |
| sa07_15_t1 ~~ sa07_15_t1 | 2.087 | 1.891 | 2.283 | 1.000 | 20.904 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 30 | 8580.065 | 8691.378 | 0.964 | 0.038 | 0.053 |
# parameters of interest
params_lcs_imagi_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_imagi_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ frequ", "frequ ~~ frequ", "imagi_t1 ~ frequ", # frequency of skill building
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.907 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.609 | NA | NA |
| d_imagi_1 ~1 | 0.465 | -0.956 | 1.886 | 1.959 | 0.641 | 0.522 |
| imagi_t1 ~1 | 4.608 | 4.544 | 4.673 | 13.032 | 139.800 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.054 | 0.009 | 0.100 | 0.967 | 2.357 | 0.018 |
| imagi_t1 ~ frequ | 0.139 | 0.030 | 0.247 | 0.300 | 2.503 | 0.012 |
| d_imagi_1 ~ imagi_t1 | -0.107 | -0.411 | 0.196 | -0.160 | -0.694 | 0.488 |
| d_imagi_1 ~ frequ | -0.016 | -0.112 | 0.079 | -0.052 | -0.331 | 0.741 |
| frequ ~~ frequ | 0.584 | 0.392 | 0.777 | 1.000 | 5.949 | 0.000 |
Run models for all facets with a template & loop:
# create df for table:
df_table_hyp7 <- bind_rows(
#traits
params_lcs_extra_ideal_specif_hyp7 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_ideal_frequ_hyp7 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_ideal_specif_hyp7 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_ideal_frequ_hyp7 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_ideal_specif_hyp7 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_ideal_frequ_hyp7 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_ideal_specif_hyp7 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_ideal_frequ_hyp7 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_ideal_specif_hyp7 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_ideal_frequ_hyp7 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_ideal_specif_hyp7 %>% filter(term=="d_socia_1 ~ sa07_01_t1"),
params_lcs_socia_ideal_frequ_hyp7 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_ideal_specif_hyp7 %>% filter(term=="d_asser_1 ~ sa07_02_t1"),
params_lcs_asser_ideal_frequ_hyp7 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_ideal_specif_hyp7 %>% filter(term=="d_energ_1 ~ sa07_03_t1"),
params_lcs_energ_ideal_frequ_hyp7 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_ideal_specif_hyp7 %>% filter(term=="d_compa_1 ~ sa07_04_t1"),
params_lcs_compa_ideal_frequ_hyp7 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_ideal_specif_hyp7 %>% filter(term=="d_respe_1 ~ sa07_05_t1"),
params_lcs_respe_ideal_frequ_hyp7 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_ideal_specif_hyp7 %>% filter(term=="d_trust_1 ~ sa07_06_t1"),
params_lcs_trust_ideal_frequ_hyp7 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_ideal_specif_hyp7 %>% filter(term=="d_organ_1 ~ sa07_07_t1"),
params_lcs_organ_ideal_frequ_hyp7 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_ideal_specif_hyp7 %>% filter(term=="d_produ_1 ~ sa07_08_t1"),
params_lcs_produ_ideal_frequ_hyp7 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_ideal_specif_hyp7 %>% filter(term=="d_respo_1 ~ sa07_09_t1"),
params_lcs_respo_ideal_frequ_hyp7 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_ideal_specif_hyp7 %>% filter(term=="d_anxie_1 ~ sa07_10_t1"),
params_lcs_anxie_ideal_frequ_hyp7 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_ideal_specif_hyp7 %>% filter(term=="d_depre_1 ~ sa07_11_t1"),
params_lcs_depre_ideal_frequ_hyp7 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_ideal_specif_hyp7 %>% filter(term=="d_volat_1 ~ sa07_12_t1"),
params_lcs_volat_ideal_frequ_hyp7 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_ideal_specif_hyp7 %>% filter(term=="d_curio_1 ~ sa07_13_t1"),
params_lcs_curio_ideal_frequ_hyp7 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_ideal_specif_hyp7 %>% filter(term=="d_aesth_1 ~ sa07_14_t1"),
params_lcs_aesth_ideal_frequ_hyp7 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_ideal_specif_hyp7 %>% filter(term=="d_imagi_1 ~ sa07_15_t1"),
params_lcs_imagi_ideal_frequ_hyp7 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, estimate, std.all, statistic, p.value)Results summary across the Big Five traits: trait-specific acceptance goals (goals) and frequency of self-acceptance behaviors (frequency) as moderators on the latent change score
kable(df_table_hyp7[1:10, ], digits = 3)| trait | moderator | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| extraversion | goals | -0.051 | -0.164 | -1.515 | 0.130 |
| extraversion | frequency | 0.037 | 0.108 | 1.176 | 0.240 |
| agreeableness | goals | -0.020 | -0.055 | -0.549 | 0.583 |
| agreeableness | frequency | 0.053 | 0.141 | 1.345 | 0.178 |
| conscientiousness | goals | -0.026 | -0.096 | -1.218 | 0.223 |
| conscientiousness | frequency | 0.064 | 0.157 | 1.595 | 0.111 |
| neuroticism | goals | 0.073 | 0.178 | 1.649 | 0.099 |
| neuroticism | frequency | -0.049 | -0.118 | -1.321 | 0.187 |
| openness | goals | 0.000 | 0.000 | -0.001 | 1.000 |
| openness | frequency | 0.022 | 0.061 | 0.628 | 0.530 |
Results summary across the Big Five facets: trait-specific acceptance goals (goals) and frequency of self-acceptance behaviors (frequency) as moderators on the latent change score
kable(df_table_hyp7[11:40, ], digits = 3)| trait | moderator | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| sociability | goals | -0.030 | -0.156 | -1.195 | 0.232 |
| sociability | frequency | -0.002 | -0.005 | -0.051 | 0.959 |
| assertiveness | goals | -0.014 | -0.098 | -1.083 | 0.279 |
| assertiveness | frequency | 0.056 | 0.208 | 1.769 | 0.077 |
| energy | goals | 0.009 | 0.116 | 0.908 | 0.364 |
| energy | frequency | -0.022 | -0.147 | -0.902 | 0.367 |
| compassion | goals | -0.037 | -0.138 | -1.238 | 0.216 |
| compassion | frequency | -0.036 | -0.075 | -0.447 | 0.655 |
| respectfulness | goals | -0.008 | -0.052 | -0.470 | 0.639 |
| respectfulness | frequency | 0.028 | 0.106 | 0.781 | 0.435 |
| trust | goals | 0.024 | 0.125 | 1.298 | 0.194 |
| trust | frequency | -0.070 | -0.197 | -1.537 | 0.124 |
| organization | goals | 0.011 | 0.073 | 0.735 | 0.462 |
| organization | frequency | -0.091 | -0.333 | -2.323 | 0.020 |
| productiveness | goals | -0.006 | -0.043 | -0.450 | 0.653 |
| productiveness | frequency | -0.061 | -0.230 | -1.620 | 0.105 |
| responsibility | goals | -0.017 | -0.067 | -0.728 | 0.467 |
| responsibility | frequency | 0.053 | 0.112 | 0.965 | 0.335 |
| anxiety | goals | 0.004 | 0.019 | 0.143 | 0.886 |
| anxiety | frequency | 0.026 | 0.063 | 0.439 | 0.661 |
| depression | goals | 0.001 | 0.008 | 0.062 | 0.950 |
| depression | frequency | 0.029 | 0.165 | 1.244 | 0.214 |
| volatility | goals | -0.008 | -0.029 | -0.305 | 0.761 |
| volatility | frequency | -0.073 | -0.173 | -1.456 | 0.145 |
| curiosity | goals | -0.022 | -0.148 | -1.098 | 0.272 |
| curiosity | frequency | 0.016 | 0.061 | 0.398 | 0.691 |
| aesthetic | goals | -0.004 | -0.120 | -0.781 | 0.435 |
| aesthetic | frequency | 0.011 | 0.191 | 0.826 | 0.409 |
| imagination | goals | 0.006 | 0.041 | 0.375 | 0.708 |
| imagination | frequency | -0.016 | -0.052 | -0.331 | 0.741 |
Prepare data frame for plotting:
df_table_hyp7_plot <- bind_rows(
#traits
params_lcs_extra_ideal_specif_hyp7 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_ideal_frequ_hyp7 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_ideal_specif_hyp7 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_ideal_frequ_hyp7 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_ideal_specif_hyp7 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_ideal_frequ_hyp7 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_ideal_specif_hyp7 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_ideal_frequ_hyp7 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_ideal_specif_hyp7 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_ideal_frequ_hyp7 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_ideal_specif_hyp7 %>% filter(term=="d_socia_1 ~ sa07_01_t1"),
params_lcs_socia_ideal_frequ_hyp7 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_ideal_specif_hyp7 %>% filter(term=="d_asser_1 ~ sa07_02_t1"),
params_lcs_asser_ideal_frequ_hyp7 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_ideal_specif_hyp7 %>% filter(term=="d_energ_1 ~ sa07_03_t1"),
params_lcs_energ_ideal_frequ_hyp7 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_ideal_specif_hyp7 %>% filter(term=="d_compa_1 ~ sa07_04_t1"),
params_lcs_compa_ideal_frequ_hyp7 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_ideal_specif_hyp7 %>% filter(term=="d_respe_1 ~ sa07_05_t1"),
params_lcs_respe_ideal_frequ_hyp7 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_ideal_specif_hyp7 %>% filter(term=="d_trust_1 ~ sa07_06_t1"),
params_lcs_trust_ideal_frequ_hyp7 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_ideal_specif_hyp7 %>% filter(term=="d_organ_1 ~ sa07_07_t1"),
params_lcs_organ_ideal_frequ_hyp7 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_ideal_specif_hyp7 %>% filter(term=="d_produ_1 ~ sa07_08_t1"),
params_lcs_produ_ideal_frequ_hyp7 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_ideal_specif_hyp7 %>% filter(term=="d_respo_1 ~ sa07_09_t1"),
params_lcs_respo_ideal_frequ_hyp7 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_ideal_specif_hyp7 %>% filter(term=="d_anxie_1 ~ sa07_10_t1"),
params_lcs_anxie_ideal_frequ_hyp7 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_ideal_specif_hyp7 %>% filter(term=="d_depre_1 ~ sa07_11_t1"),
params_lcs_depre_ideal_frequ_hyp7 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_ideal_specif_hyp7 %>% filter(term=="d_volat_1 ~ sa07_12_t1"),
params_lcs_volat_ideal_frequ_hyp7 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_ideal_specif_hyp7 %>% filter(term=="d_curio_1 ~ sa07_13_t1"),
params_lcs_curio_ideal_frequ_hyp7 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_ideal_specif_hyp7 %>% filter(term=="d_aesth_1 ~ sa07_14_t1"),
params_lcs_aesth_ideal_frequ_hyp7 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_ideal_specif_hyp7 %>% filter(term=="d_imagi_1 ~ sa07_15_t1"),
params_lcs_imagi_ideal_frequ_hyp7 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp7_plot <- df_table_hyp7_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor("ideal", levels = c("ideal"), labels = c("ideal"))) %>%
mutate(moderator = factor(moderator, levels = c("goals", "frequency"), labels = c("goals", "frequency"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_hyp7_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + moderator, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
We will explore a) whether change in psychological well-being indicators as well as the difference between real- and ideal- self will differ across groups at follow-up.
Life satisfaction: fitting multi-group models
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)
# configural invariance
mi_lcs_swls_group_config <- '
swls_t1 =~ 1*sw06_01_t1 + c("lamb2a", "lamb2b")*sw06_02_t1 + c("lamb3a", "lamb3b")*sw06_03_t1 + c("lamb4a", "lamb4b")*sw06_04_t1 # This specifies the measurement model for swls_t1
swls_t2 =~ 1*sw06_01_t2 + c("lamb2a", "lamb2b")*sw06_02_t2 + c("lamb3a", "lamb3b")*sw06_03_t2 + c("lamb4a", "lamb4b")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadings
swls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1
d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2
swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0
swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0
d_swls_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
swls_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of swls_t1
d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores
swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1
d_swls_1 ~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parameter
sw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2
sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2
sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2
sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2
sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1
sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1
sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1
sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1
sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2
sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2
sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2
sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2
sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
sw06_02_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
sw06_03_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
sw06_04_t1 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T1
sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
sw06_02_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
sw06_03_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
sw06_04_t2 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T2
'
lcs_swls_group_config <- sem(mi_lcs_swls_group_config, data=df_sbsa_wide_wb, estimator='mlr',
fixed.x=FALSE, missing='fiml', group = "rando")
# weak invariance
mi_lcs_swls_group_weak <- '
swls_t1 =~ 1*sw06_01_t1 + c("lamb2", "lamb2")*sw06_02_t1 + c("lamb3", "lamb3")*sw06_03_t1 + c("lamb4", "lamb4")*sw06_04_t1 # This specifies the measurement model for swls_t1
swls_t2 =~ 1*sw06_01_t2 + c("lamb2", "lamb2")*sw06_02_t2 + c("lamb3", "lamb3")*sw06_03_t2 + c("lamb4", "lamb4")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadings
swls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1
d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2
swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0
swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0
d_swls_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
swls_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of swls_t1
d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores
swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1
d_swls_1 ~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parameter
sw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2
sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2
sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2
sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2
sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1
sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1
sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1
sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1
sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2
sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2
sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2
sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2
sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
sw06_02_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
sw06_03_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
sw06_04_t1 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T1
sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
sw06_02_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
sw06_03_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
sw06_04_t2 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T2
'
lcs_swls_group_weak <- sem(mi_lcs_swls_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = "loadings")
# strong invariance
mi_lcs_swls_group_strong <- '
swls_t1 =~ 1*sw06_01_t1 + c("lamb2", "lamb2")*sw06_02_t1 + c("lamb3", "lamb3")*sw06_03_t1 + c("lamb4", "lamb4")*sw06_04_t1 # This specifies the measurement model for swls_t1
swls_t2 =~ 1*sw06_01_t2 + c("lamb2", "lamb2")*sw06_02_t2 + c("lamb3", "lamb3")*sw06_03_t2 + c("lamb4", "lamb4")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadings
swls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1
d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2
swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0
swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0
d_swls_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score
swls_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of swls_t1
d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores
swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1
d_swls_1 ~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parameter
sw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2
sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2
sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2
sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2
sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1
sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1
sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1
sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1
sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2
sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2
sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2
sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2
sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
sw06_02_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1
sw06_03_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1
sw06_04_t1 ~ c("m4", "m4")*1 # This estimates the intercept of X4 at T1
sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
sw06_02_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2
sw06_03_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2
sw06_04_t2 ~ c("m4", "m4")*1 # This estimates the intercept of X4 at T2
'
lcs_swls_group_strong <- sem(mi_lcs_swls_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = c("intercepts", "loadings"))Life satisfaction: results
# model comparison tests for measurement invariance
lavTestLRT(lcs_swls_group_config, lcs_swls_group_weak, lcs_swls_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_swls_group_config 50 12581 12749 93.676
lcs_swls_group_weak 53 12581 12736 99.658 6.2538 3 0.09989 .
lcs_swls_group_strong 58 12575 12708 103.548 3.8751 5 0.56754
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# show model with varying latent change parameters
# -> key parameter is "d_swls_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Skill-Building group)
kable(broom::tidy(lcs_swls_group_weak, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_swls_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_swls_1 ~1 | d_int_a | 0.799 | 1.283 | 5.517 | 0 |
| d_swls_1 ~1 | d_int_b | 0.700 | 1.051 | 5.060 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(lcs_swls_group_strong, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_swls_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_swls_1 ~1 | d_int | 0.752 | 1.219 | 7.526 | 0 |
| d_swls_1 ~1 | d_int | 0.752 | 1.120 | 7.526 | 0 |
# whole model (weak invariance)
summary(lcs_swls_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 62 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 58
Number of equality constraints 23
Number of observations per group: Used Total
Self-Acceptance 302 302
Skill-Building 316 317
Number of missing patterns per group:
Self-Acceptance 3
Skill-Building 3
Model Test User Model:
Standard Scaled
Test Statistic 99.658 89.396
Degrees of freedom 53 53
P-value (Chi-square) 0.000 0.001
Scaling correction factor 1.115
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Self-Acceptance 55.830 50.081
Skill-Building 43.827 39.314
Model Test Baseline Model:
Test statistic 3077.617 2174.072
Degrees of freedom 56 56
P-value 0.000 0.000
Scaling correction factor 1.416
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.985 0.983
Tucker-Lewis Index (TLI) 0.984 0.982
Robust Comparative Fit Index (CFI) 0.987
Robust Tucker-Lewis Index (TLI) 0.986
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -6255.406 -6255.406
Scaling correction factor 0.752
for the MLR correction
Loglikelihood unrestricted model (H1) -6205.577 -6205.577
Scaling correction factor 1.167
for the MLR correction
Akaike (AIC) 12580.812 12580.812
Bayesian (BIC) 12735.739 12735.739
Sample-size adjusted Bayesian (SABIC) 12624.620 12624.620
Root Mean Square Error of Approximation:
RMSEA 0.053 0.047
90 Percent confidence interval - lower 0.037 0.030
90 Percent confidence interval - upper 0.069 0.063
P-value H_0: RMSEA <= 0.050 0.347 0.597
P-value H_0: RMSEA >= 0.080 0.002 0.000
Robust RMSEA 0.050
90 Percent confidence interval - lower 0.031
90 Percent confidence interval - upper 0.068
P-value H_0: Robust RMSEA <= 0.050 0.482
P-value H_0: Robust RMSEA >= 0.080 0.002
Standardized Root Mean Square Residual:
SRMR 0.042 0.042
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Self-Acceptance]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
swls_t1 =~
s06_01_ 1.000 0.984 0.822
s06_02_ (lmb2) 0.831 0.031 26.671 0.000 0.817 0.737
s06_03_ (lmb3) 1.033 0.035 29.654 0.000 1.016 0.829
s06_04_ (lmb4) 0.870 0.036 24.278 0.000 0.856 0.710
swls_t2 =~
s06_01_ 1.000 1.010 0.829
s06_02_ (lmb2) 0.831 0.031 26.671 0.000 0.839 0.746
s06_03_ (lmb3) 1.033 0.035 29.654 0.000 1.044 0.836
s06_04_ (lmb4) 0.870 0.036 24.278 0.000 0.879 0.719
d_swls_1 =~
swls_t2 1.000 0.616 0.616
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
swls_t2 ~
swls_t1 1.000 0.974 0.974
d_swls_1 ~
swls_t1 (fb_a) -0.173 0.046 -3.742 0.000 -0.273 -0.273
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.sw06_01_t1 ~~
.s06_01_ (cov1) 0.146 0.067 2.189 0.029 0.146 0.315
.sw06_02_t1 ~~
.s06_02_ (cov2) 0.204 0.049 4.162 0.000 0.204 0.363
.sw06_03_t1 ~~
.s06_03_ (cov3) 0.143 0.042 3.378 0.001 0.143 0.304
.sw06_04_t1 ~~
.s06_04_ (cov4) 0.309 0.052 5.905 0.000 0.309 0.430
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.swls_t2 0.000 0.000 0.000
.d_swl_1 (d_n_) 0.799 0.145 5.517 0.000 1.283 1.283
swls_t1 (wb__) 2.689 0.065 41.116 0.000 2.734 2.734
.s06_01_ 0.000 0.000 0.000
.s06_02_ (m2a) 0.597 0.103 5.822 0.000 0.597 0.538
.s06_03_ (m3a) -0.051 0.116 -0.437 0.662 -0.051 -0.041
.s06_04_ (m4a) 0.399 0.117 3.397 0.001 0.399 0.331
.s06_01_ 0.000 0.000 0.000
.s06_02_ (m2a) 0.597 0.103 5.822 0.000 0.597 0.531
.s06_03_ (m3a) -0.051 0.116 -0.437 0.662 -0.051 -0.041
.s06_04_ (m4a) 0.399 0.117 3.397 0.001 0.399 0.327
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.swls_t2 0.000 0.000 0.000
.d_swl_1 (d_v_) 0.359 0.058 6.174 0.000 0.925 0.925
swls_t1 (wb__) 0.967 0.074 13.072 0.000 1.000 1.000
.s06_01_ (res1) 0.465 0.071 6.555 0.000 0.465 0.325
.s06_02_ (res2) 0.562 0.048 11.663 0.000 0.562 0.457
.s06_03_ (res3) 0.470 0.056 8.419 0.000 0.470 0.313
.s06_04_ (res4) 0.720 0.055 13.130 0.000 0.720 0.496
.s06_01_ (res1) 0.465 0.071 6.555 0.000 0.465 0.313
.s06_02_ (res2) 0.562 0.048 11.663 0.000 0.562 0.444
.s06_03_ (res3) 0.470 0.056 8.419 0.000 0.470 0.301
.s06_04_ (res4) 0.720 0.055 13.130 0.000 0.720 0.482
Group 2 [Skill-Building]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
swls_t1 =~
s06_01_ 1.000 0.951 0.840
s06_02_ (lmb2) 0.831 0.031 26.671 0.000 0.790 0.710
s06_03_ (lmb3) 1.033 0.035 29.654 0.000 0.983 0.842
s06_04_ (lmb4) 0.870 0.036 24.278 0.000 0.828 0.688
swls_t2 =~
s06_01_ 1.000 1.037 0.861
s06_02_ (lmb2) 0.831 0.031 26.671 0.000 0.862 0.739
s06_03_ (lmb3) 1.033 0.035 29.654 0.000 1.072 0.862
s06_04_ (lmb4) 0.870 0.036 24.278 0.000 0.903 0.719
d_swls_1 =~
swls_t2 1.000 0.642 0.642
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
swls_t2 ~
swls_t1 1.000 0.917 0.917
d_swls_1 ~
swls_t1 (fb_b) -0.150 0.044 -3.394 0.001 -0.214 -0.214
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.sw06_01_t1 ~~
.s06_01_ (cv1b) 0.059 0.043 1.375 0.169 0.059 0.155
.sw06_02_t1 ~~
.s06_02_ (cv2b) 0.352 0.050 7.058 0.000 0.352 0.572
.sw06_03_t1 ~~
.s06_03_ (cv3b) 0.040 0.041 0.980 0.327 0.040 0.100
.sw06_04_t1 ~~
.s06_04_ (cv4b) 0.324 0.063 5.119 0.000 0.324 0.425
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.swls_t2 0.000 0.000 0.000
.d_swl_1 (d_n_) 0.700 0.138 5.060 0.000 1.051 1.051
swls_t1 (wb__) 2.709 0.063 43.214 0.000 2.849 2.849
.s06_01_ 0.000 0.000 0.000
.s06_02_ (m2b) 0.560 0.105 5.352 0.000 0.560 0.503
.s06_03_ (m3b) 0.020 0.112 0.176 0.860 0.020 0.017
.s06_04_ (m4b) 0.469 0.115 4.063 0.000 0.469 0.390
.s06_01_ 0.000 0.000 0.000
.s06_02_ (m2b) 0.560 0.105 5.352 0.000 0.560 0.480
.s06_03_ (m3b) 0.020 0.112 0.176 0.860 0.020 0.016
.s06_04_ (m4b) 0.469 0.115 4.063 0.000 0.469 0.373
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.swls_t2 0.000 0.000 0.000
.d_swl_1 (d_v_) 0.423 0.054 7.828 0.000 0.954 0.954
swls_t1 (wb__) 0.904 0.077 11.737 0.000 1.000 1.000
.s06_01_ (rs1b) 0.377 0.047 8.005 0.000 0.377 0.294
.s06_02_ (rs2b) 0.616 0.051 12.008 0.000 0.616 0.497
.s06_03_ (rs3b) 0.397 0.041 9.620 0.000 0.397 0.291
.s06_04_ (rs4b) 0.763 0.064 11.969 0.000 0.763 0.527
.s06_01_ (rs1b) 0.377 0.047 8.005 0.000 0.377 0.259
.s06_02_ (rs2b) 0.616 0.051 12.008 0.000 0.616 0.453
.s06_03_ (rs3b) 0.397 0.041 9.620 0.000 0.397 0.257
.s06_04_ (rs4b) 0.763 0.064 11.969 0.000 0.763 0.483Meaning in life: fitting multi-group models
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)
# configural invariance
mi_lcs_meaning_group_config <- '
meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2a", "lamb2b")*meaning_par2_t1 + c("lamb3a", "lamb3b")*meaning_par3_t1 # This specifies the measurement model for meaning_t1
meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2a", "lamb2b")*meaning_par2_t2 + c("lamb3a", "lamb3b")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadings
meaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1
d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2
meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0
meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0
d_meaning_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
meaning_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of meaning_t1
d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores
meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1
d_meaning_1 ~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parameter
meaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1
meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1
meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1
meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2
meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2
meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2
meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
meaning_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
meaning_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
meaning_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
meaning_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_meaning_group_config <- sem(mi_lcs_meaning_group_config, data=df_sbsa_wide_wb, estimator='mlr',
fixed.x=FALSE, missing='fiml', group = "rando")
# weak invariance
mi_lcs_meaning_group_weak <- '
meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2", "lamb2")*meaning_par2_t1 + c("lamb3", "lamb3")*meaning_par3_t1 # This specifies the measurement model for meaning_t1
meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2", "lamb2")*meaning_par2_t2 + c("lamb3", "lamb3")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadings
meaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1
d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2
meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0
meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0
d_meaning_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
meaning_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of meaning_t1
d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores
meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1
d_meaning_1 ~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parameter
meaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1
meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1
meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1
meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2
meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2
meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2
meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
meaning_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
meaning_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
meaning_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
meaning_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_meaning_group_weak <- sem(mi_lcs_meaning_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = "loadings")
# strong invariance
mi_lcs_meaning_group_strong <- '
meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2", "lamb2")*meaning_par2_t1 + c("lamb3", "lamb3")*meaning_par3_t1 # This specifies the measurement model for meaning_t1
meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2", "lamb2")*meaning_par2_t2 + c("lamb3", "lamb3")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadings
meaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1
d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2
meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0
meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0
d_meaning_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score
meaning_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of meaning_t1
d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores
meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1
d_meaning_1 ~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parameter
meaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1
meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1
meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1
meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2
meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2
meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2
meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
meaning_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1
meaning_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1
meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
meaning_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2
meaning_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2
'
lcs_meaning_group_strong <- sem(mi_lcs_meaning_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = c("intercepts", "loadings"))Meaning in life: results
# model comparison tests for measurement invariance
lavTestLRT(lcs_meaning_group_config, lcs_meaning_group_weak, lcs_meaning_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_meaning_group_config 24 9277.2 9410.0 36.570
lcs_meaning_group_weak 26 9273.3 9397.3 36.734 0.1454 2 0.9299
lcs_meaning_group_strong 30 9269.0 9375.3 40.431 3.5145 4 0.4757# show model with varying latent change parameters
# -> key parameter is "d_meaning_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Skill-Building group)
kable(broom::tidy(lcs_meaning_group_weak, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_meaning_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_meaning_1 ~1 | d_int_a | 1.328 | 1.644 | 7.171 | 0 |
| d_meaning_1 ~1 | d_int_b | 1.381 | 1.698 | 7.117 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(lcs_meaning_group_strong, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_meaning_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_meaning_1 ~1 | d_int | 1.353 | 1.664 | 10.113 | 0 |
| d_meaning_1 ~1 | d_int | 1.353 | 1.671 | 10.113 | 0 |
# whole model (weak invariance)
summary(lcs_meaning_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 70 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 44
Number of equality constraints 16
Number of observations per group: Used Total
Self-Acceptance 302 302
Skill-Building 316 317
Number of missing patterns per group:
Self-Acceptance 3
Skill-Building 3
Model Test User Model:
Standard Scaled
Test Statistic 36.734 34.860
Degrees of freedom 26 26
P-value (Chi-square) 0.079 0.115
Scaling correction factor 1.054
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Self-Acceptance 18.358 17.421
Skill-Building 18.376 17.438
Model Test Baseline Model:
Test statistic 2765.913 2212.132
Degrees of freedom 30 30
P-value 0.000 0.000
Scaling correction factor 1.250
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.996 0.996
Tucker-Lewis Index (TLI) 0.995 0.995
Robust Comparative Fit Index (CFI) 0.997
Robust Tucker-Lewis Index (TLI) 0.996
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4608.675 -4608.675
Scaling correction factor 0.759
for the MLR correction
Loglikelihood unrestricted model (H1) -4590.308 -4590.308
Scaling correction factor 1.126
for the MLR correction
Akaike (AIC) 9273.350 9273.350
Bayesian (BIC) 9397.292 9397.292
Sample-size adjusted Bayesian (SABIC) 9308.397 9308.397
Root Mean Square Error of Approximation:
RMSEA 0.037 0.033
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.062 0.059
P-value H_0: RMSEA <= 0.050 0.785 0.844
P-value H_0: RMSEA >= 0.080 0.001 0.001
Robust RMSEA 0.035
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.062
P-value H_0: Robust RMSEA <= 0.050 0.798
P-value H_0: Robust RMSEA >= 0.080 0.002
Standardized Root Mean Square Residual:
SRMR 0.031 0.031
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Self-Acceptance]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
meaning_t1 =~
mnn_1_1 1.000 0.980 0.793
mnn_2_1 (lmb2) 1.190 0.038 31.128 0.000 1.166 0.937
mnn_3_1 (lmb3) 1.169 0.039 30.259 0.000 1.145 0.869
meaning_t2 =~
mnn_1_2 1.000 0.998 0.798
mnn_2_2 (lmb2) 1.190 0.038 31.128 0.000 1.187 0.939
mnn_3_2 (lmb3) 1.169 0.039 30.259 0.000 1.166 0.872
d_meaning_1 =~
mnng_t2 1.000 0.810 0.810
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
meaning_t2 ~
mnng_t1 1.000 0.982 0.982
d_meaning_1 ~
mnng_t1 (fb_a) -0.322 0.055 -5.858 0.000 -0.390 -0.390
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.meaning_par1_t1 ~~
.mnn_1_2 (cov1) 0.311 0.044 7.017 0.000 0.311 0.549
.meaning_par2_t1 ~~
.mnn_2_2 (cov2) 0.009 0.036 0.243 0.808 0.009 0.046
.meaning_par3_t1 ~~
.mnn_3_2 (cov3) 0.084 0.040 2.095 0.036 0.084 0.196
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.mnng_t2 0.000 0.000 0.000
.d_mnn_1 (d_n_) 1.328 0.185 7.171 0.000 1.644 1.644
mnng_t1 (wb__) 3.312 0.068 48.642 0.000 3.379 3.379
.mnn_1_1 0.000 0.000 0.000
.mnn_2_1 (m2a) -0.182 0.138 -1.321 0.186 -0.182 -0.147
.mnn_3_1 (m3a) -0.096 0.144 -0.663 0.508 -0.096 -0.073
.mnn_1_2 0.000 0.000 0.000
.mnn_2_2 (m2a) -0.182 0.138 -1.321 0.186 -0.182 -0.144
.mnn_3_2 (m3a) -0.096 0.144 -0.663 0.508 -0.096 -0.072
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.mnng_t2 0.000 0.000 0.000
.d_mnn_1 (d_v_) 0.553 0.074 7.475 0.000 0.848 0.848
mnng_t1 (wb__) 0.960 0.096 10.027 0.000 1.000 1.000
.mnn_1_1 (res1) 0.567 0.045 12.562 0.000 0.567 0.371
.mnn_2_1 (res2) 0.188 0.034 5.519 0.000 0.188 0.121
.mnn_3_1 (res3) 0.427 0.042 10.082 0.000 0.427 0.246
.mnn_1_2 (res1) 0.567 0.045 12.562 0.000 0.567 0.363
.mnn_2_2 (res2) 0.188 0.034 5.519 0.000 0.188 0.118
.mnn_3_2 (res3) 0.427 0.042 10.082 0.000 0.427 0.239
Group 2 [Skill-Building]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
meaning_t1 =~
mnn_1_1 1.000 0.913 0.772
mnn_2_1 (lmb2) 1.190 0.038 31.128 0.000 1.086 0.913
mnn_3_1 (lmb3) 1.169 0.039 30.259 0.000 1.067 0.862
meaning_t2 =~
mnn_1_2 1.000 0.965 0.789
mnn_2_2 (lmb2) 1.190 0.038 31.128 0.000 1.149 0.921
mnn_3_2 (lmb3) 1.169 0.039 30.259 0.000 1.128 0.874
d_meaning_1 =~
mnng_t2 1.000 0.843 0.843
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
meaning_t2 ~
mnng_t1 1.000 0.946 0.946
d_meaning_1 ~
mnng_t1 (fb_b) -0.338 0.055 -6.166 0.000 -0.379 -0.379
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.meaning_par1_t1 ~~
.mnn_1_2 (cv1b) 0.332 0.052 6.433 0.000 0.332 0.587
.meaning_par2_t1 ~~
.mnn_2_2 (cv2b) 0.048 0.036 1.337 0.181 0.048 0.205
.meaning_par3_t1 ~~
.mnn_3_2 (cv3b) 0.090 0.039 2.327 0.020 0.090 0.229
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.mnng_t2 0.000 0.000 0.000
.d_mnn_1 (d_n_) 1.381 0.194 7.117 0.000 1.698 1.698
mnng_t1 (wb__) 3.151 0.063 49.784 0.000 3.452 3.452
.mnn_1_1 0.000 0.000 0.000
.mnn_2_1 (m2b) -0.072 0.133 -0.539 0.590 -0.072 -0.060
.mnn_3_1 (m3b) 0.015 0.141 0.107 0.915 0.015 0.012
.mnn_1_2 0.000 0.000 0.000
.mnn_2_2 (m2b) -0.072 0.133 -0.539 0.590 -0.072 -0.057
.mnn_3_2 (m3b) 0.015 0.141 0.107 0.915 0.015 0.012
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.mnng_t2 0.000 0.000 0.000
.d_mnn_1 (d_v_) 0.567 0.082 6.936 0.000 0.856 0.856
mnng_t1 (wb__) 0.833 0.093 8.944 0.000 1.000 1.000
.mnn_1_1 (rs1b) 0.566 0.053 10.780 0.000 0.566 0.405
.mnn_2_1 (rs2b) 0.235 0.038 6.200 0.000 0.235 0.166
.mnn_3_1 (rs3b) 0.394 0.046 8.546 0.000 0.394 0.257
.mnn_1_2 (rs1b) 0.566 0.053 10.780 0.000 0.566 0.378
.mnn_2_2 (rs2b) 0.235 0.038 6.200 0.000 0.235 0.151
.mnn_3_2 (rs3b) 0.394 0.046 8.546 0.000 0.394 0.236Self-esteem: fitting multi-group models
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)
# configural invariance
mi_lcs_selfes_group_config <- '
selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2a", "lamb2b")*selfes_par2_t1 + c("lamb3a", "lamb3b")*selfes_par3_t1 # This specifies the measurement model for selfes_t1
selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2a", "lamb2b")*selfes_par2_t2 + c("lamb3a", "lamb3b")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadings
selfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1
d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2
selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0
selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0
d_selfes_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
selfes_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of selfes_t1
d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores
selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1
d_selfes_1 ~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameter
selfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1
selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1
selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1
selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2
selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2
selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2
selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
selfes_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
selfes_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
selfes_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
selfes_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_selfes_group_config <- sem(mi_lcs_selfes_group_config, data=df_sbsa_wide_wb, estimator='mlr',
fixed.x=FALSE, missing='fiml', group = "rando")
# weak invariance
mi_lcs_selfes_group_weak <- '
selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2", "lamb2")*selfes_par2_t1 + c("lamb3", "lamb3")*selfes_par3_t1 # This specifies the measurement model for selfes_t1
selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2", "lamb2")*selfes_par2_t2 + c("lamb3", "lamb3")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadings
selfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1
d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2
selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0
selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0
d_selfes_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
selfes_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of selfes_t1
d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores
selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1
d_selfes_1 ~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameter
selfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1
selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1
selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1
selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2
selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2
selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2
selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
selfes_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
selfes_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
selfes_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
selfes_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_selfes_group_weak <- sem(mi_lcs_selfes_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = "loadings")
# strong invariance
mi_lcs_selfes_group_strong <- '
selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2", "lamb2")*selfes_par2_t1 + c("lamb3", "lamb3")*selfes_par3_t1 # This specifies the measurement model for selfes_t1
selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2", "lamb2")*selfes_par2_t2 + c("lamb3", "lamb3")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadings
selfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1
d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2
selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0
selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0
d_selfes_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score
selfes_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of selfes_t1
d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores
selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1
d_selfes_1 ~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameter
selfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1
selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1
selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1
selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2
selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2
selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2
selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
selfes_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1
selfes_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1
selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
selfes_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2
selfes_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2
'
lcs_selfes_group_strong <- sem(mi_lcs_selfes_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = c("intercepts", "loadings"))Meaning in life: results
# model comparison tests for measurement invariance
lavTestLRT(lcs_selfes_group_config, lcs_selfes_group_weak, lcs_selfes_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_selfes_group_config 24 6491.3 6624.1 21.406
lcs_selfes_group_weak 26 6489.5 6613.4 23.582 2.2801 2 0.3198
lcs_selfes_group_strong 30 6487.1 6593.4 29.256 5.4364 4 0.2454# show model with varying latent change parameters
# -> key parameter is "d_selfes_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Skill-Building group)
kable(broom::tidy(lcs_selfes_group_weak, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_selfes_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_selfes_1 ~1 | d_int_a | 1.100 | 2.035 | 7.109 | 0 |
| d_selfes_1 ~1 | d_int_b | 0.933 | 1.783 | 6.481 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(lcs_selfes_group_strong, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_selfes_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_selfes_1 ~1 | d_int | 1.027 | 1.924 | 9.608 | 0 |
| d_selfes_1 ~1 | d_int | 1.027 | 1.931 | 9.608 | 0 |
# whole model (weak invariance)
summary(lcs_selfes_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 76 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 44
Number of equality constraints 16
Number of observations per group: Used Total
Self-Acceptance 302 302
Skill-Building 316 317
Number of missing patterns per group:
Self-Acceptance 3
Skill-Building 3
Model Test User Model:
Standard Scaled
Test Statistic 23.583 22.661
Degrees of freedom 26 26
P-value (Chi-square) 0.600 0.652
Scaling correction factor 1.041
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Self-Acceptance 10.544 10.132
Skill-Building 13.038 12.529
Model Test Baseline Model:
Test statistic 3538.853 2838.977
Degrees of freedom 30 30
P-value 0.000 0.000
Scaling correction factor 1.247
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.001 1.001
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.001
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3216.737 -3216.737
Scaling correction factor 0.688
for the MLR correction
Loglikelihood unrestricted model (H1) -3204.946 -3204.946
Scaling correction factor 1.062
for the MLR correction
Akaike (AIC) 6489.475 6489.475
Bayesian (BIC) 6613.417 6613.417
Sample-size adjusted Bayesian (SABIC) 6524.522 6524.522
Root Mean Square Error of Approximation:
RMSEA 0.000 0.000
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.040 0.037
P-value H_0: RMSEA <= 0.050 0.989 0.993
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.038
P-value H_0: Robust RMSEA <= 0.050 0.990
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.018 0.018
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Self-Acceptance]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
selfes_t1 =~
slf_1_1 1.000 0.826 0.892
slf_2_1 (lmb2) 1.140 0.026 44.124 0.000 0.942 0.926
slf_3_1 (lmb3) 1.157 0.027 42.509 0.000 0.956 0.923
selfes_t2 =~
slf_1_2 1.000 0.791 0.884
slf_2_2 (lmb2) 1.140 0.026 44.124 0.000 0.902 0.920
slf_3_2 (lmb3) 1.157 0.027 42.509 0.000 0.915 0.917
d_selfes_1 =~
slfs_t2 1.000 0.683 0.683
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
selfes_t2 ~
slfs_t1 1.000 1.044 1.044
d_selfes_1 ~
slfs_t1 (fb_a) -0.256 0.043 -5.882 0.000 -0.391 -0.391
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.selfes_par1_t1 ~~
.slf_1_2 (cov1) 0.072 0.014 5.083 0.000 0.072 0.411
.selfes_par2_t1 ~~
.slf_2_2 (cov2) 0.057 0.014 4.007 0.000 0.057 0.390
.selfes_par3_t1 ~~
.slf_3_2 (cov3) 0.036 0.015 2.465 0.014 0.036 0.229
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.slfs_t2 0.000 0.000 0.000
.d_slf_1 (d_n_) 1.100 0.155 7.109 0.000 2.035 2.035
slfs_t1 (wb__) 3.149 0.053 58.941 0.000 3.813 3.813
.slf_1_1 0.000 0.000 0.000
.slf_2_1 (m2a) -0.466 0.089 -5.244 0.000 -0.466 -0.458
.slf_3_1 (m3a) -0.458 0.089 -5.130 0.000 -0.458 -0.442
.slf_1_2 0.000 0.000 0.000
.slf_2_2 (m2a) -0.466 0.089 -5.244 0.000 -0.466 -0.475
.slf_3_2 (m3a) -0.458 0.089 -5.130 0.000 -0.458 -0.458
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.slfs_t2 0.000 0.000 0.000
.d_slf_1 (d_v_) 0.248 0.033 7.568 0.000 0.847 0.847
slfs_t1 (wb__) 0.682 0.052 13.185 0.000 1.000 1.000
.slf_1_1 (res1) 0.175 0.016 11.105 0.000 0.175 0.204
.slf_2_1 (res2) 0.147 0.018 8.174 0.000 0.147 0.142
.slf_3_1 (res3) 0.159 0.016 9.755 0.000 0.159 0.148
.slf_1_2 (res1) 0.175 0.016 11.105 0.000 0.175 0.219
.slf_2_2 (res2) 0.147 0.018 8.174 0.000 0.147 0.153
.slf_3_2 (res3) 0.159 0.016 9.755 0.000 0.159 0.159
Group 2 [Skill-Building]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
selfes_t1 =~
slf_1_1 1.000 0.730 0.864
slf_2_1 (lmb2) 1.140 0.026 44.124 0.000 0.833 0.911
slf_3_1 (lmb3) 1.157 0.027 42.509 0.000 0.845 0.888
selfes_t2 =~
slf_1_2 1.000 0.765 0.874
slf_2_2 (lmb2) 1.140 0.026 44.124 0.000 0.873 0.918
slf_3_2 (lmb3) 1.157 0.027 42.509 0.000 0.885 0.897
d_selfes_1 =~
slfs_t2 1.000 0.684 0.684
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
selfes_t2 ~
slfs_t1 1.000 0.954 0.954
d_selfes_1 ~
slfs_t1 (fb_b) -0.208 0.043 -4.865 0.000 -0.290 -0.290
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.selfes_par1_t1 ~~
.slf_1_2 (cv1b) 0.069 0.015 4.544 0.000 0.069 0.380
.selfes_par2_t1 ~~
.slf_2_2 (cv2b) 0.013 0.014 0.949 0.342 0.013 0.091
.selfes_par3_t1 ~~
.slf_3_2 (cv3b) 0.054 0.019 2.849 0.004 0.054 0.282
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.slfs_t2 0.000 0.000 0.000
.d_slf_1 (d_n_) 0.933 0.144 6.481 0.000 1.783 1.783
slfs_t1 (wb__) 3.113 0.046 67.561 0.000 4.263 4.263
.slf_1_1 0.000 0.000 0.000
.slf_2_1 (m2b) -0.390 0.087 -4.465 0.000 -0.390 -0.427
.slf_3_1 (m3b) -0.450 0.094 -4.780 0.000 -0.450 -0.473
.slf_1_2 0.000 0.000 0.000
.slf_2_2 (m2b) -0.390 0.087 -4.465 0.000 -0.390 -0.411
.slf_3_2 (m3b) -0.450 0.094 -4.780 0.000 -0.450 -0.456
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.slfs_t2 0.000 0.000 0.000
.d_slf_1 (d_v_) 0.251 0.031 8.086 0.000 0.916 0.916
slfs_t1 (wb__) 0.533 0.043 12.431 0.000 1.000 1.000
.slf_1_1 (rs1b) 0.181 0.017 10.802 0.000 0.181 0.253
.slf_2_1 (rs2b) 0.141 0.015 9.477 0.000 0.141 0.169
.slf_3_1 (rs3b) 0.191 0.019 10.192 0.000 0.191 0.211
.slf_1_2 (rs1b) 0.181 0.017 10.802 0.000 0.181 0.236
.slf_2_2 (rs2b) 0.141 0.015 9.477 0.000 0.141 0.157
.slf_3_2 (rs3b) 0.191 0.019 10.192 0.000 0.191 0.196Self-concept clarity: fitting multi-group models
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)
# configural invariance
mi_lcs_concept_group_config <- '
concept_t1 =~ 1*concept_par1_t1 + c("lamb2a", "lamb2b")*concept_par2_t1 + c("lamb3a", "lamb3b")*concept_par3_t1 # This specifies the measurement model for concept_t1
concept_t2 =~ 1*concept_par1_t2 + c("lamb2a", "lamb2b")*concept_par2_t2 + c("lamb3a", "lamb3b")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadings
concept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1
d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2
concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0
concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0
d_concept_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
concept_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of concept_t1
d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores
concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1
d_concept_1 ~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameter
concept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1
concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1
concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1
concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2
concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2
concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2
concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
concept_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
concept_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
concept_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
concept_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_concept_group_config <- sem(mi_lcs_concept_group_config, data=df_sbsa_wide_wb, estimator='mlr',
fixed.x=FALSE, missing='fiml', group = "rando")
# weak invariance
mi_lcs_concept_group_weak <- '
concept_t1 =~ 1*concept_par1_t1 + c("lamb2", "lamb2")*concept_par2_t1 + c("lamb3", "lamb3")*concept_par3_t1 # This specifies the measurement model for concept_t1
concept_t2 =~ 1*concept_par1_t2 + c("lamb2", "lamb2")*concept_par2_t2 + c("lamb3", "lamb3")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadings
concept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1
d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2
concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0
concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0
d_concept_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
concept_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of concept_t1
d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores
concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1
d_concept_1 ~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameter
concept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1
concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1
concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1
concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2
concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2
concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2
concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
concept_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
concept_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
concept_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
concept_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_concept_group_weak <- sem(mi_lcs_concept_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = "loadings")
# strong invariance
mi_lcs_concept_group_strong <- '
concept_t1 =~ 1*concept_par1_t1 + c("lamb2", "lamb2")*concept_par2_t1 + c("lamb3", "lamb3")*concept_par3_t1 # This specifies the measurement model for concept_t1
concept_t2 =~ 1*concept_par1_t2 + c("lamb2", "lamb2")*concept_par2_t2 + c("lamb3", "lamb3")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadings
concept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1
d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2
concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0
concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0
d_concept_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score
concept_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of concept_t1
d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores
concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1
d_concept_1 ~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameter
concept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1
concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1
concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1
concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2
concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2
concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2
concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
concept_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1
concept_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1
concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
concept_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2
concept_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2
'
lcs_concept_group_strong <- sem(mi_lcs_concept_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = c("intercepts", "loadings"))Meaning in life: results
# model comparison tests for measurement invariance
lavTestLRT(lcs_concept_group_config, lcs_concept_group_weak, lcs_concept_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_concept_group_config 24 7539.6 7672.4 26.360
lcs_concept_group_weak 26 7536.2 7660.1 26.953 0.51746 2 0.7720
lcs_concept_group_strong 30 7530.2 7636.4 28.930 1.99827 4 0.7361# show model with varying latent change parameters
# -> key parameter is "d_concept_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Skill-Building group)
kable(broom::tidy(lcs_concept_group_weak, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_concept_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_concept_1 ~1 | d_int_a | 1.148 | 1.974 | 7.532 | 0 |
| d_concept_1 ~1 | d_int_b | 1.036 | 1.906 | 6.734 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(lcs_concept_group_strong, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_concept_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_concept_1 ~1 | d_int | 1.095 | 1.902 | 10.081 | 0 |
| d_concept_1 ~1 | d_int | 1.095 | 1.998 | 10.081 | 0 |
# whole model (weak invariance)
summary(lcs_concept_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 68 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 44
Number of equality constraints 16
Number of observations per group: Used Total
Self-Acceptance 302 302
Skill-Building 316 317
Number of missing patterns per group:
Self-Acceptance 3
Skill-Building 3
Model Test User Model:
Standard Scaled
Test Statistic 26.952 26.131
Degrees of freedom 26 26
P-value (Chi-square) 0.412 0.456
Scaling correction factor 1.031
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Self-Acceptance 21.040 20.398
Skill-Building 5.912 5.732
Model Test Baseline Model:
Test statistic 2476.067 1974.067
Degrees of freedom 30 30
P-value 0.000 0.000
Scaling correction factor 1.254
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.000 1.000
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.000
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3740.086 -3740.086
Scaling correction factor 0.711
for the MLR correction
Loglikelihood unrestricted model (H1) -3726.610 -3726.610
Scaling correction factor 1.076
for the MLR correction
Akaike (AIC) 7536.173 7536.173
Bayesian (BIC) 7660.115 7660.115
Sample-size adjusted Bayesian (SABIC) 7571.220 7571.220
Root Mean Square Error of Approximation:
RMSEA 0.011 0.004
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.047 0.045
P-value H_0: RMSEA <= 0.050 0.968 0.977
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.046
P-value H_0: Robust RMSEA <= 0.050 0.971
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.021 0.021
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Self-Acceptance]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
concept_t1 =~
cnc_1_1 1.000 0.720 0.818
cnc_2_1 (lmb2) 1.216 0.045 27.266 0.000 0.876 0.870
cnc_3_1 (lmb3) 1.239 0.041 30.273 0.000 0.892 0.881
concept_t2 =~
cnc_1_2 1.000 0.714 0.816
cnc_2_2 (lmb2) 1.216 0.045 27.266 0.000 0.869 0.869
cnc_3_2 (lmb3) 1.239 0.041 30.273 0.000 0.885 0.879
d_concept_1 =~
cncpt_2 1.000 0.814 0.814
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
concept_t2 ~
cncpt_1 1.000 1.008 1.008
d_concept_1 ~
cncpt_1 (fb_a) -0.334 0.049 -6.802 0.000 -0.413 -0.413
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.concept_par1_t1 ~~
.cnc_1_2 (cov1) 0.043 0.019 2.205 0.027 0.043 0.166
.concept_par2_t1 ~~
.cnc_2_2 (cov2) 0.081 0.024 3.424 0.001 0.081 0.329
.concept_par3_t1 ~~
.cnc_3_2 (cov3) 0.086 0.023 3.689 0.000 0.086 0.372
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.cncpt_2 0.000 0.000 0.000
.d_cnc_1 (d_n_) 1.148 0.152 7.532 0.000 1.974 1.974
cncpt_1 (wb__) 2.950 0.047 62.128 0.000 4.097 4.097
.cnc_1_1 0.000 0.000 0.000
.cnc_2_1 (m2a) -0.420 0.142 -2.947 0.003 -0.420 -0.417
.cnc_3_1 (m3a) -0.785 0.127 -6.207 0.000 -0.785 -0.775
.cnc_1_2 0.000 0.000 0.000
.cnc_2_2 (m2a) -0.420 0.142 -2.947 0.003 -0.420 -0.419
.cnc_3_2 (m3a) -0.785 0.127 -6.207 0.000 -0.785 -0.780
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.cncpt_2 0.000 0.000 0.000
.d_cnc_1 (d_v_) 0.280 0.037 7.479 0.000 0.829 0.829
cncpt_1 (wb__) 0.519 0.046 11.360 0.000 1.000 1.000
.cnc_1_1 (res1) 0.256 0.019 13.538 0.000 0.256 0.331
.cnc_2_1 (res2) 0.245 0.025 9.971 0.000 0.245 0.242
.cnc_3_1 (res3) 0.230 0.025 9.213 0.000 0.230 0.224
.cnc_1_2 (res1) 0.256 0.019 13.538 0.000 0.256 0.334
.cnc_2_2 (res2) 0.245 0.025 9.971 0.000 0.245 0.245
.cnc_3_2 (res3) 0.230 0.025 9.213 0.000 0.230 0.227
Group 2 [Skill-Building]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
concept_t1 =~
cnc_1_1 1.000 0.651 0.773
cnc_2_1 (lmb2) 1.216 0.045 27.266 0.000 0.792 0.858
cnc_3_1 (lmb3) 1.239 0.041 30.273 0.000 0.807 0.853
concept_t2 =~
cnc_1_2 1.000 0.699 0.795
cnc_2_2 (lmb2) 1.216 0.045 27.266 0.000 0.851 0.874
cnc_3_2 (lmb3) 1.239 0.041 30.273 0.000 0.867 0.869
d_concept_1 =~
cncpt_2 1.000 0.777 0.777
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
concept_t2 ~
cncpt_1 1.000 0.931 0.931
d_concept_1 ~
cncpt_1 (fb_b) -0.271 0.049 -5.482 0.000 -0.325 -0.325
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.concept_par1_t1 ~~
.cnc_1_2 (cv1b) 0.115 0.023 5.086 0.000 0.115 0.405
.concept_par2_t1 ~~
.cnc_2_2 (cv2b) 0.058 0.025 2.322 0.020 0.058 0.258
.concept_par3_t1 ~~
.cnc_3_2 (cv3b) 0.081 0.026 3.077 0.002 0.081 0.332
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.cncpt_2 0.000 0.000 0.000
.d_cnc_1 (d_n_) 1.036 0.154 6.734 0.000 1.906 1.906
cncpt_1 (wb__) 2.901 0.045 63.801 0.000 4.455 4.455
.cnc_1_1 0.000 0.000 0.000
.cnc_2_1 (m2b) -0.397 0.138 -2.871 0.004 -0.397 -0.430
.cnc_3_1 (m3b) -0.722 0.126 -5.736 0.000 -0.722 -0.763
.cnc_1_2 0.000 0.000 0.000
.cnc_2_2 (m2b) -0.397 0.138 -2.871 0.004 -0.397 -0.408
.cnc_3_2 (m3b) -0.722 0.126 -5.736 0.000 -0.722 -0.723
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.cncpt_2 0.000 0.000 0.000
.d_cnc_1 (d_v_) 0.264 0.033 8.063 0.000 0.894 0.894
cncpt_1 (wb__) 0.424 0.041 10.272 0.000 1.000 1.000
.cnc_1_1 (rs1b) 0.285 0.024 11.794 0.000 0.285 0.402
.cnc_2_1 (rs2b) 0.224 0.026 8.761 0.000 0.224 0.263
.cnc_3_1 (rs3b) 0.244 0.031 7.846 0.000 0.244 0.273
.cnc_1_2 (rs1b) 0.285 0.024 11.794 0.000 0.285 0.368
.cnc_2_2 (rs2b) 0.224 0.026 8.761 0.000 0.224 0.237
.cnc_3_2 (rs3b) 0.244 0.031 7.846 0.000 0.244 0.245Profile correlations by group and measurement occasion (mixed effects models) - results:
df_sbsa <- df_sbsa %>% mutate(time_d = time - 1)
psych::describeBy(df_sbsa$profile_corr_item_z, list(df_sbsa$rando, df_sbsa$time_d))
Descriptive statistics by group
: Self-Acceptance
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 298 0.38 0.49 0.37 0.37 0.47 -1.05 1.86 2.91 0.19 0.26 0.03
------------------------------------------------------------
: Skill-Building
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 311 0.38 0.47 0.35 0.35 0.44 -0.71 2.65 3.36 0.78 1.94 0.03
------------------------------------------------------------
: Self-Acceptance
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 300 0.54 0.57 0.5 0.52 0.54 -0.9 3.44 4.34 0.92 3.17 0.03
------------------------------------------------------------
: Skill-Building
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 315 0.56 0.49 0.52 0.55 0.48 -0.77 2.65 3.42 0.42 0.91 0.03psych::describeBy(df_sbsa$profile_corr_facet_z, list(df_sbsa$rando, df_sbsa$time_d))
Descriptive statistics by group
: Self-Acceptance
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 298 0.28 0.67 0.21 0.26 0.64 -1.5 2.56 4.06 0.31 0.25 0.04
------------------------------------------------------------
: Skill-Building
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 311 0.28 0.65 0.23 0.25 0.65 -1.06 2.65 3.71 0.61 0.56 0.04
------------------------------------------------------------
: Self-Acceptance
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 300 0.48 0.76 0.45 0.45 0.74 -1.17 3.94 5.11 0.66 1.56 0.04
------------------------------------------------------------
: Skill-Building
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 315 0.52 0.7 0.49 0.5 0.77 -1.22 2.65 3.87 0.23 -0.2 0.04lm_profile_corr_item <- lmerTest::lmer(profile_corr_item_z ~ time_d * rando + (1 | pid), data = df_sbsa)
lm_profile_corr_facet <- lmerTest::lmer(profile_corr_facet_z ~ time_d * rando + (1 | pid), data = df_sbsa)
kable(bind_rows(as.data.frame(summary(lm_profile_corr_item)$coefficients), as.data.frame(summary(lm_profile_corr_facet)$coefficients)) %>% as_tibble() %>% mutate(outcome = c(rep("Item-level profile corr.", 4), rep("Facet-level profile corr.", 4)), term = c(rep(c("Intercept", "time", "group", "time*group"), 2))) %>% select(outcome, term, everything()) %>% rename(p = `Pr(>|t|)`), digits = 3)| outcome | term | Estimate | Std. Error | df | t value | p |
|---|---|---|---|---|---|---|
| Item-level profile corr. | Intercept | 0.375 | 0.029 | 841.717 | 12.823 | 0.000 |
| Item-level profile corr. | time | 0.166 | 0.023 | 607.282 | 7.130 | 0.000 |
| Item-level profile corr. | group | 0.002 | 0.041 | 843.314 | 0.055 | 0.956 |
| Item-level profile corr. | time*group | 0.015 | 0.033 | 607.714 | 0.456 | 0.649 |
| Facet-level profile corr. | Intercept | 0.277 | 0.040 | 830.493 | 6.881 | 0.000 |
| Facet-level profile corr. | time | 0.204 | 0.031 | 607.089 | 6.516 | 0.000 |
| Facet-level profile corr. | group | 0.008 | 0.056 | 832.054 | 0.148 | 0.882 |
| Facet-level profile corr. | time*group | 0.028 | 0.044 | 607.505 | 0.630 | 0.529 |
Squared trait-/facet-differences by group and measurement occasion (mixed effects models) - results:
mod_traits_sqdiff_groups <- df_sbsa %>%
select(pid, time_d, rando, ends_with("_sqdiff")) %>%
pivot_longer(ends_with("_sqdiff"),
names_to = "test", names_prefix = "facet", values_to = "score", values_drop_na = TRUE) %>%
group_by(pid, test) %>%
mutate(assessments = n()) %>%
ungroup() %>%
filter(assessments==2) %>%
select(-assessments) %>%
group_nest(test) %>%
mutate(lmer_mods = map(data, ~lmerTest::lmer(score ~ time_d * rando + (1 | pid), data = .x))) %>%
pull(lmer_mods) %>%
purrr::set_names(names(b5_vars))
mod_traits_sqdiff_groups_unlist <- as.data.frame(summary(mod_traits_sqdiff_groups[[1]])$coefficients) %>% as_tibble()
for (i in 2:length(mod_traits_sqdiff_groups)) {
mod_traits_sqdiff_groups_unlist <- bind_rows(mod_traits_sqdiff_groups_unlist,
as.data.frame(summary(mod_traits_sqdiff_groups[[i]])$coefficients) %>% as_tibble())
}
kable(mod_traits_sqdiff_groups_unlist %>%
mutate(outcome = rep(names(b5_vars), each=4),
term = c(rep(c("Intercept", "time", "group", "time*group"), 20))) %>%
select(outcome, term, everything()) %>% rename(p = `Pr(>|t|)`),
digits = 3)| outcome | term | Estimate | Std. Error | df | t value | p |
|---|---|---|---|---|---|---|
| extraversion | Intercept | 0.447 | 0.046 | 1126.156 | 9.747 | 0.000 |
| extraversion | time | -0.115 | 0.056 | 603.000 | -2.076 | 0.038 |
| extraversion | group | -0.031 | 0.064 | 1126.156 | -0.478 | 0.633 |
| extraversion | time*group | 0.050 | 0.078 | 603.000 | 0.637 | 0.524 |
| agreeableness | Intercept | 0.570 | 0.048 | 894.007 | 11.857 | 0.000 |
| agreeableness | time | -0.143 | 0.044 | 603.000 | -3.285 | 0.001 |
| agreeableness | group | -0.043 | 0.067 | 894.007 | -0.636 | 0.525 |
| agreeableness | time*group | -0.038 | 0.061 | 603.000 | -0.628 | 0.531 |
| conscientiousness | Intercept | 3.986 | 0.235 | 872.522 | 16.937 | 0.000 |
| conscientiousness | time | -0.546 | 0.206 | 603.000 | -2.655 | 0.008 |
| conscientiousness | group | 0.081 | 0.329 | 872.522 | 0.245 | 0.807 |
| conscientiousness | time*group | -0.187 | 0.288 | 603.000 | -0.649 | 0.516 |
| neuroticism | Intercept | 1.827 | 0.124 | 976.557 | 14.764 | 0.000 |
| neuroticism | time | -0.493 | 0.126 | 603.000 | -3.928 | 0.000 |
| neuroticism | group | -0.121 | 0.173 | 976.557 | -0.697 | 0.486 |
| neuroticism | time*group | 0.096 | 0.176 | 603.000 | 0.547 | 0.584 |
| openness | Intercept | 0.596 | 0.050 | 1006.579 | 11.976 | 0.000 |
| openness | time | -0.176 | 0.052 | 603.000 | -3.354 | 0.001 |
| openness | group | -0.125 | 0.070 | 1006.579 | -1.792 | 0.073 |
| openness | time*group | 0.042 | 0.073 | 603.000 | 0.576 | 0.565 |
| sociability | Intercept | 1.844 | 0.112 | 852.645 | 16.510 | 0.000 |
| sociability | time | -0.379 | 0.094 | 603.000 | -4.024 | 0.000 |
| sociability | group | -0.304 | 0.156 | 852.645 | -1.946 | 0.052 |
| sociability | time*group | 0.097 | 0.132 | 603.000 | 0.739 | 0.460 |
| assertiveness | Intercept | 0.519 | 0.056 | 1129.612 | 9.312 | 0.000 |
| assertiveness | time | -0.106 | 0.068 | 603.000 | -1.568 | 0.117 |
| assertiveness | group | 0.049 | 0.078 | 1129.612 | 0.634 | 0.526 |
| assertiveness | time*group | -0.087 | 0.095 | 603.000 | -0.913 | 0.362 |
| energy | Intercept | 4.252 | 0.231 | 811.998 | 18.391 | 0.000 |
| energy | time | -0.989 | 0.180 | 603.000 | -5.494 | 0.000 |
| energy | group | -0.190 | 0.324 | 811.998 | -0.586 | 0.558 |
| energy | time*group | 0.100 | 0.252 | 603.000 | 0.395 | 0.693 |
| compassion | Intercept | 2.043 | 0.140 | 960.187 | 14.624 | 0.000 |
| compassion | time | -0.435 | 0.139 | 603.000 | -3.133 | 0.002 |
| compassion | group | 0.094 | 0.196 | 960.187 | 0.482 | 0.630 |
| compassion | time*group | -0.098 | 0.194 | 603.000 | -0.503 | 0.615 |
| respectfulness | Intercept | 1.845 | 0.107 | 883.675 | 17.273 | 0.000 |
| respectfulness | time | -0.511 | 0.095 | 603.000 | -5.374 | 0.000 |
| respectfulness | group | -0.016 | 0.149 | 883.675 | -0.107 | 0.915 |
| respectfulness | time*group | 0.038 | 0.133 | 603.000 | 0.284 | 0.776 |
| trust | Intercept | 1.283 | 0.110 | 895.578 | 11.637 | 0.000 |
| trust | time | -0.299 | 0.100 | 603.000 | -2.990 | 0.003 |
| trust | group | -0.059 | 0.154 | 895.578 | -0.379 | 0.704 |
| trust | time*group | 0.153 | 0.140 | 603.000 | 1.091 | 0.276 |
| organization | Intercept | 3.387 | 0.192 | 781.307 | 17.638 | 0.000 |
| organization | time | -0.660 | 0.139 | 603.000 | -4.741 | 0.000 |
| organization | group | -0.008 | 0.269 | 781.307 | -0.029 | 0.977 |
| organization | time*group | -0.046 | 0.195 | 603.000 | -0.236 | 0.813 |
| productiveness | Intercept | 0.461 | 0.040 | 964.821 | 11.596 | 0.000 |
| productiveness | time | -0.099 | 0.040 | 603.000 | -2.489 | 0.013 |
| productiveness | group | -0.001 | 0.056 | 964.821 | -0.010 | 0.992 |
| productiveness | time*group | 0.005 | 0.056 | 603.000 | 0.086 | 0.931 |
| responsibility | Intercept | 2.153 | 0.146 | 913.370 | 14.752 | 0.000 |
| responsibility | time | -0.484 | 0.136 | 603.000 | -3.562 | 0.000 |
| responsibility | group | -0.440 | 0.204 | 913.370 | -2.156 | 0.031 |
| responsibility | time*group | 0.183 | 0.190 | 603.000 | 0.961 | 0.337 |
| anxiety | Intercept | 3.116 | 0.179 | 840.814 | 17.365 | 0.000 |
| anxiety | time | -0.531 | 0.148 | 603.000 | -3.585 | 0.000 |
| anxiety | group | -0.391 | 0.251 | 840.814 | -1.556 | 0.120 |
| anxiety | time*group | -0.013 | 0.207 | 603.000 | -0.062 | 0.951 |
| depression | Intercept | 0.620 | 0.061 | 955.530 | 10.154 | 0.000 |
| depression | time | -0.110 | 0.060 | 603.000 | -1.816 | 0.070 |
| depression | group | 0.064 | 0.085 | 955.530 | 0.749 | 0.454 |
| depression | time*group | -0.092 | 0.084 | 603.000 | -1.084 | 0.279 |
| volatility | Intercept | 1.309 | 0.101 | 925.284 | 13.019 | 0.000 |
| volatility | time | -0.264 | 0.095 | 603.000 | -2.764 | 0.006 |
| volatility | group | -0.125 | 0.141 | 925.284 | -0.887 | 0.375 |
| volatility | time*group | 0.033 | 0.133 | 603.000 | 0.247 | 0.805 |
| curiosity | Intercept | 2.889 | 0.172 | 857.042 | 16.819 | 0.000 |
| curiosity | time | -0.840 | 0.146 | 603.000 | -5.747 | 0.000 |
| curiosity | group | -0.041 | 0.240 | 857.042 | -0.171 | 0.864 |
| curiosity | time*group | 0.103 | 0.204 | 603.000 | 0.506 | 0.613 |
| aesthetic | Intercept | 1.216 | 0.098 | 1004.161 | 12.459 | 0.000 |
| aesthetic | time | -0.277 | 0.103 | 603.000 | -2.707 | 0.007 |
| aesthetic | group | 0.024 | 0.137 | 1004.161 | 0.176 | 0.860 |
| aesthetic | time*group | -0.144 | 0.143 | 603.000 | -1.004 | 0.316 |
| imagination | Intercept | 2.938 | 0.201 | 820.936 | 14.635 | 0.000 |
| imagination | time | -0.635 | 0.159 | 603.000 | -3.987 | 0.000 |
| imagination | group | 0.057 | 0.281 | 820.936 | 0.203 | 0.839 |
| imagination | time*group | -0.097 | 0.223 | 603.000 | -0.435 | 0.664 |
We will explore b) whether the extent of change/acceptance is related to personal project dimension variables.
For now, I use the personal project dimension variables assessed at T1:
They were, however, also assessed at T2:
Reshape and split data set by intervention group:
df_sbsa_wide_pers_sb_ppd <- df_sbsa %>%
filter(rando=="Skill-Building") %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sb01"))) %>% # Personal project dimensions - skill building
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with(c("sb01")))) %>%
select(-c(sb01_01_t2, sb01_02_t2))
# colnames(df_sbsa_wide_pers_sb_ppd)
group_assign <- df_sbsa %>% select(pid, rando) %>% unique()
df_sbsa_wide_pers_sb_ppd <- df_sbsa_wide_pers %>% left_join(group_assign) %>% filter(rando=="Skill-Building") %>% select(-rando) %>%
left_join(df_sbsa_wide_pers_sb_ppd)
# need to form a mean score because some models did not converge when using the latent factor of PPD (but high correlation between the two items)
df_sbsa_wide_pers_sb_ppd <- df_sbsa_wide_pers_sb_ppd %>% mutate(ppd = rowMeans(across(c(sb01_01_t1, sb01_02_t1)), na.rm=T))Run models for all traits with a template & loop:
# create templates:
# 1st, for facet-specific change goals
trait_template_ppd_skill <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadings
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~ ppd # This estimates the moderation effect on personality at T1
d_trait_1 ~ trait_t1 + ppd # This estimates the self-feedback parameter and the moderation effect on the change score
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ppd ~~ ppd
ppd ~ 1
'
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_ppd_skill,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"),
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_ppd")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_ppd")), trait_model_fit))
} Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 19 | 4081.961 | 4153.38 | 0.994 | 0.038 | 0.034 |
# parameters of interest
params_lcs_extra_curr_ppd <- broom::tidy(fit_mi_lcs_extra_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ ppd", "ppd ~~ ppd", "extra_t1 ~ ppd", # change ppd
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.994 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.569 | NA | NA |
| d_extra_1 ~1 | 0.313 | 0.021 | 0.604 | 0.941 | 2.102 | 0.036 |
| extra_t1 ~1 | 3.344 | 3.077 | 3.612 | 5.757 | 24.491 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.100 | 0.063 | 0.136 | 0.901 | 5.315 | 0.000 |
| extra_t1 ~ ppd | -0.172 | -0.246 | -0.098 | -0.275 | -4.569 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.133 | -0.218 | -0.049 | -0.233 | -3.084 | 0.002 |
| d_extra_1 ~ ppd | 0.056 | 0.003 | 0.110 | 0.158 | 2.082 | 0.037 |
| ppd ~~ ppd | 0.862 | 0.742 | 0.983 | 1.000 | 14.009 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 19 | 3679.044 | 3750.463 | 0.982 | 0.053 | 0.038 |
# parameters of interest
params_lcs_agree_curr_ppd <- broom::tidy(fit_mi_lcs_agree_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ ppd", "ppd ~~ ppd", "agree_t1 ~ ppd", # change ppd
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.056 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.542 | NA | NA |
| d_agree_1 ~1 | 0.746 | 0.405 | 1.088 | 3.413 | 4.286 | 0.000 |
| agree_t1 ~1 | 3.494 | 3.253 | 3.736 | 8.197 | 28.354 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.042 | 0.019 | 0.064 | 0.869 | 3.677 | 0.000 |
| agree_t1 ~ ppd | -0.044 | -0.110 | 0.022 | -0.096 | -1.310 | 0.190 |
| d_agree_1 ~ agree_t1 | -0.186 | -0.273 | -0.099 | -0.363 | -4.204 | 0.000 |
| d_agree_1 ~ ppd | -0.014 | -0.050 | 0.022 | -0.061 | -0.776 | 0.438 |
| ppd ~~ ppd | 0.863 | 0.742 | 0.984 | 1.000 | 13.993 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 19 | 3833.166 | 3904.586 | 0.999 | 0.015 | 0.016 |
# parameters of interest
params_lcs_consc_curr_ppd <- broom::tidy(fit_mi_lcs_consc_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ ppd", "ppd ~~ ppd", "consc_t1 ~ ppd", # change ppd
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.040 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.453 | NA | NA |
| d_consc_1 ~1 | 0.525 | 0.197 | 0.853 | 1.629 | 3.137 | 0.002 |
| consc_t1 ~1 | 4.070 | 3.765 | 4.376 | 5.501 | 26.102 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.094 | 0.059 | 0.129 | 0.906 | 5.294 | 0.000 |
| consc_t1 ~ ppd | -0.233 | -0.320 | -0.146 | -0.292 | -5.232 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.130 | -0.198 | -0.062 | -0.299 | -3.766 | 0.000 |
| d_consc_1 ~ ppd | 0.007 | -0.042 | 0.057 | 0.022 | 0.300 | 0.764 |
| ppd ~~ ppd | 0.864 | 0.743 | 0.985 | 1.000 | 13.973 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 19 | 4405.752 | 4477.171 | 0.988 | 0.059 | 0.028 |
# parameters of interest
params_lcs_neuro_curr_ppd <- broom::tidy(fit_mi_lcs_neuro_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ ppd", "ppd ~~ ppd", "neuro_t1 ~ ppd", # change ppd
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.014 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.603 | NA | NA |
| d_neuro_1 ~1 | 0.539 | 0.251 | 0.826 | 1.088 | 3.675 | 0.000 |
| neuro_t1 ~1 | 2.376 | 2.008 | 2.744 | 2.852 | 12.657 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.218 | 0.148 | 0.288 | 0.889 | 6.109 | 0.000 |
| neuro_t1 ~ ppd | 0.337 | 0.236 | 0.439 | 0.376 | 6.519 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.170 | -0.249 | -0.090 | -0.285 | -4.187 | 0.000 |
| d_neuro_1 ~ ppd | -0.051 | -0.117 | 0.015 | -0.095 | -1.507 | 0.132 |
| ppd ~~ ppd | 0.862 | 0.742 | 0.983 | 1.000 | 14.019 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 19 | 3974.892 | 4046.312 | 0.994 | 0.033 | 0.023 |
# parameters of interest
params_lcs_openn_curr_ppd <- broom::tidy(fit_mi_lcs_openn_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ ppd", "ppd ~~ ppd", "openn_t1 ~ ppd", # change ppd
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.913 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.394 | NA | NA |
| d_openn_1 ~1 | 0.027 | -0.303 | 0.357 | 0.143 | 0.161 | 0.872 |
| openn_t1 ~1 | 3.545 | 3.323 | 3.768 | 8.046 | 31.180 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.036 | 0.014 | 0.058 | 0.999 | 3.250 | 0.001 |
| openn_t1 ~ ppd | 0.002 | -0.060 | 0.064 | 0.005 | 0.068 | 0.945 |
| d_openn_1 ~ openn_t1 | 0.006 | -0.079 | 0.091 | 0.015 | 0.146 | 0.884 |
| d_openn_1 ~ ppd | 0.004 | -0.036 | 0.044 | 0.020 | 0.204 | 0.838 |
| ppd ~~ ppd | 0.862 | 0.742 | 0.983 | 1.000 | 14.007 | 0.000 |
Run models for all facets with a template & loop:
# create templates:
# 1st, for facet-specific change goal
facet_template_ppd_skill <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~ ppd # This estimates the moderation effect on personality at T1
d_facet_1 ~ facet_t1 + ppd # This estimates the self-feedback parameter and the moderation effect on the change score
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ppd ~~ ppd
ppd ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[5]], item_nrs)
template_filled <- str_replace_all(facet_template_ppd_skill,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sb_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_ppd")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_ppd")), facet_model_fit))
} Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7621.367 | 7707.822 | 0.949 | 0.079 | 0.052 |
# parameters of interest
params_lcs_socia_curr_ppd <- broom::tidy(fit_mi_lcs_socia_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ ppd", "ppd ~~ ppd", "d_socia_1 ~ ppd", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.013 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.632 | NA | NA |
| d_socia_1 ~1 | 0.526 | 0.100 | 0.953 | 0.987 | 2.422 | 0.015 |
| socia_t1 ~1 | 3.173 | 2.728 | 3.618 | 3.712 | 13.969 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.249 | 0.149 | 0.349 | 0.875 | 4.871 | 0.000 |
| d_socia_1 ~ socia_t1 | -0.198 | -0.297 | -0.099 | -0.317 | -3.926 | 0.000 |
| d_socia_1 ~ ppd | 0.069 | -0.025 | 0.163 | 0.120 | 1.446 | 0.148 |
| ppd ~~ ppd | 0.863 | 0.742 | 0.983 | 1.000 | 14.008 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7950.19 | 8036.644 | 1 | 0 | 0.036 |
# parameters of interest
params_lcs_asser_curr_ppd <- broom::tidy(fit_mi_lcs_asser_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ ppd", "ppd ~~ ppd", "d_asser_1 ~ ppd", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.000 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.353 | NA | NA |
| d_asser_1 ~1 | -0.035 | -0.468 | 0.399 | -0.172 | -0.157 | 0.876 |
| asser_t1 ~1 | 3.553 | 3.251 | 3.856 | 6.223 | 23.012 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.037 | -0.006 | 0.079 | 0.899 | 1.703 | 0.089 |
| d_asser_1 ~ asser_t1 | -0.038 | -0.154 | 0.077 | -0.109 | -0.652 | 0.514 |
| d_asser_1 ~ ppd | 0.059 | -0.002 | 0.121 | 0.272 | 1.882 | 0.060 |
| ppd ~~ ppd | 0.863 | 0.742 | 0.984 | 1.000 | 13.998 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7874.662 | 7961.116 | 0.965 | 0.057 | 0.041 |
# parameters of interest
params_lcs_energ_curr_ppd <- broom::tidy(fit_mi_lcs_energ_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ ppd", "ppd ~~ ppd", "d_energ_1 ~ ppd", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.991 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.561 | NA | NA |
| d_energ_1 ~1 | 0.326 | 0.039 | 0.612 | 1.303 | 2.228 | 0.026 |
| energ_t1 ~1 | 2.084 | 1.826 | 2.342 | 4.725 | 15.829 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.058 | 0.017 | 0.098 | 0.925 | 2.791 | 0.005 |
| d_energ_1 ~ energ_t1 | -0.138 | -0.252 | -0.024 | -0.243 | -2.363 | 0.018 |
| d_energ_1 ~ ppd | -0.018 | -0.067 | 0.031 | -0.065 | -0.702 | 0.483 |
| ppd ~~ ppd | 0.862 | 0.741 | 0.983 | 1.000 | 14.009 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7672.655 | 7759.11 | 0.983 | 0.032 | 0.042 |
# parameters of interest
params_lcs_compa_curr_ppd <- broom::tidy(fit_mi_lcs_compa_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ ppd", "ppd ~~ ppd", "d_compa_1 ~ ppd", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.147 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.349 | NA | NA |
| d_compa_1 ~1 | 0.641 | 0.086 | 1.197 | 3.065 | 2.263 | 0.024 |
| compa_t1 ~1 | 4.268 | 3.878 | 4.658 | 6.200 | 21.443 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.029 | -0.033 | 0.092 | 0.670 | 0.921 | 0.357 |
| d_compa_1 ~ compa_t1 | -0.164 | -0.283 | -0.045 | -0.540 | -2.707 | 0.007 |
| d_compa_1 ~ ppd | 0.040 | -0.023 | 0.103 | 0.176 | 1.230 | 0.219 |
| ppd ~~ ppd | 0.862 | 0.742 | 0.983 | 1.000 | 14.006 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7014.829 | 7101.284 | 0.947 | 0.068 | 0.055 |
# parameters of interest
params_lcs_respe_curr_ppd <- broom::tidy(fit_mi_lcs_respe_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ ppd", "ppd ~~ ppd", "d_respe_1 ~ ppd", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.097 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.600 | NA | NA |
| d_respe_1 ~1 | 1.243 | 0.689 | 1.797 | 3.901 | 4.396 | 0.000 |
| respe_t1 ~1 | 4.558 | 4.248 | 4.869 | 7.821 | 28.774 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.082 | 0.041 | 0.122 | 0.804 | 3.980 | 0.000 |
| d_respe_1 ~ respe_t1 | -0.239 | -0.350 | -0.128 | -0.436 | -4.211 | 0.000 |
| d_respe_1 ~ ppd | -0.039 | -0.089 | 0.010 | -0.114 | -1.551 | 0.121 |
| ppd ~~ ppd | 0.862 | 0.742 | 0.983 | 1.000 | 14.007 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 8030.473 | 8116.928 | 0.94 | 0.067 | 0.051 |
# parameters of interest
params_lcs_trust_curr_ppd <- broom::tidy(fit_mi_lcs_trust_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ ppd", "ppd ~~ ppd", "d_trust_1 ~ ppd", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.031 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.561 | NA | NA |
| d_trust_1 ~1 | 0.392 | -0.055 | 0.840 | 1.189 | 1.718 | 0.086 |
| trust_t1 ~1 | 2.745 | 2.409 | 3.080 | 4.526 | 16.026 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.097 | 0.016 | 0.179 | 0.893 | 2.332 | 0.020 |
| d_trust_1 ~ trust_t1 | -0.178 | -0.317 | -0.038 | -0.327 | -2.496 | 0.013 |
| d_trust_1 ~ ppd | -0.003 | -0.067 | 0.062 | -0.007 | -0.078 | 0.938 |
| ppd ~~ ppd | 0.863 | 0.742 | 0.983 | 1.000 | 14.006 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7594.782 | 7681.237 | 0.98 | 0.052 | 0.034 |
# parameters of interest
params_lcs_organ_curr_ppd <- broom::tidy(fit_mi_lcs_organ_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ ppd", "ppd ~~ ppd", "d_organ_1 ~ ppd", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.086 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.524 | NA | NA |
| d_organ_1 ~1 | 0.520 | 0.215 | 0.824 | 1.005 | 3.347 | 0.001 |
| organ_t1 ~1 | 1.966 | 1.477 | 2.454 | 1.835 | 7.889 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.224 | 0.122 | 0.325 | 0.836 | 4.332 | 0.000 |
| d_organ_1 ~ organ_t1 | -0.185 | -0.264 | -0.105 | -0.382 | -4.546 | 0.000 |
| d_organ_1 ~ ppd | -0.042 | -0.121 | 0.037 | -0.075 | -1.036 | 0.300 |
| ppd ~~ ppd | 0.864 | 0.743 | 0.985 | 1.000 | 13.994 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7532.131 | 7618.586 | 0.971 | 0.056 | 0.037 |
# parameters of interest
params_lcs_produ_curr_ppd <- broom::tidy(fit_mi_lcs_produ_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ ppd", "ppd ~~ ppd", "d_produ_1 ~ ppd", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.044 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.480 | NA | NA |
| d_produ_1 ~1 | 0.393 | 0.049 | 0.738 | 1.118 | 2.237 | 0.025 |
| produ_t1 ~1 | 2.359 | 1.972 | 2.746 | 3.085 | 11.951 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.110 | 0.053 | 0.167 | 0.891 | 3.796 | 0.000 |
| d_produ_1 ~ produ_t1 | -0.133 | -0.240 | -0.027 | -0.290 | -2.453 | 0.014 |
| d_produ_1 ~ ppd | -0.033 | -0.101 | 0.036 | -0.087 | -0.940 | 0.347 |
| ppd ~~ ppd | 0.863 | 0.742 | 0.984 | 1.000 | 13.998 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7597.449 | 7683.904 | 0.922 | 0.083 | 0.08 |
# parameters of interest
params_lcs_respo_curr_ppd <- broom::tidy(fit_mi_lcs_respo_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ ppd", "ppd ~~ ppd", "d_respo_1 ~ ppd", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.997 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.435 | NA | NA |
| d_respo_1 ~1 | 0.574 | 0.128 | 1.021 | 2.855 | 2.523 | 0.012 |
| respo_t1 ~1 | 3.984 | 3.747 | 4.221 | 8.636 | 32.907 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.037 | 0.004 | 0.071 | 0.925 | 2.164 | 0.031 |
| d_respo_1 ~ respo_t1 | -0.109 | -0.210 | -0.007 | -0.249 | -2.099 | 0.036 |
| d_respo_1 ~ ppd | -0.038 | -0.085 | 0.008 | -0.177 | -1.605 | 0.108 |
| ppd ~~ ppd | 0.864 | 0.742 | 0.985 | 1.000 | 13.974 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7938.628 | 8025.083 | 0.983 | 0.039 | 0.039 |
# parameters of interest
params_lcs_anxie_curr_ppd <- broom::tidy(fit_mi_lcs_anxie_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ ppd", "ppd ~~ ppd", "d_anxie_1 ~ ppd", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.076 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.669 | NA | NA |
| d_anxie_1 ~1 | 0.753 | 0.259 | 1.247 | 1.309 | 2.987 | 0.003 |
| anxie_t1 ~1 | 3.825 | 3.385 | 4.265 | 4.137 | 17.043 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.271 | 0.127 | 0.414 | 0.819 | 3.699 | 0.000 |
| d_anxie_1 ~ anxie_t1 | -0.247 | -0.364 | -0.130 | -0.397 | -4.141 | 0.000 |
| d_anxie_1 ~ ppd | 0.046 | -0.038 | 0.130 | 0.074 | 1.067 | 0.286 |
| ppd ~~ ppd | 0.863 | 0.742 | 0.983 | 1.000 | 14.008 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7762.354 | 7848.809 | 0.949 | 0.086 | 0.059 |
# parameters of interest
params_lcs_depre_curr_ppd <- broom::tidy(fit_mi_lcs_depre_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ ppd", "ppd ~~ ppd", "d_depre_1 ~ ppd", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.957 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.546 | NA | NA |
| d_depre_1 ~1 | 0.228 | -0.184 | 0.640 | 0.596 | 1.083 | 0.279 |
| depre_t1 ~1 | 3.917 | 3.577 | 4.258 | 5.860 | 22.543 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.137 | 0.076 | 0.199 | 0.942 | 4.378 | 0.000 |
| d_depre_1 ~ depre_t1 | -0.086 | -0.178 | 0.006 | -0.151 | -1.835 | 0.067 |
| d_depre_1 ~ ppd | 0.056 | -0.006 | 0.118 | 0.135 | 1.760 | 0.078 |
| ppd ~~ ppd | 0.862 | 0.742 | 0.983 | 1.000 | 14.020 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7923.883 | 8010.337 | 0.989 | 0.036 | 0.031 |
# parameters of interest
params_lcs_volat_curr_ppd <- broom::tidy(fit_mi_lcs_volat_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ ppd", "ppd ~~ ppd", "d_volat_1 ~ ppd", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.073 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.601 | NA | NA |
| d_volat_1 ~1 | 0.550 | 0.230 | 0.871 | 1.101 | 3.364 | 0.001 |
| volat_t1 ~1 | 2.317 | 1.901 | 2.734 | 2.596 | 10.903 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.210 | 0.133 | 0.287 | 0.842 | 5.360 | 0.000 |
| d_volat_1 ~ volat_t1 | -0.218 | -0.296 | -0.141 | -0.390 | -5.508 | 0.000 |
| d_volat_1 ~ ppd | -0.014 | -0.093 | 0.066 | -0.025 | -0.336 | 0.737 |
| ppd ~~ ppd | 0.863 | 0.742 | 0.983 | 1.000 | 14.005 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7710.009 | 7796.464 | 0.977 | 0.04 | 0.046 |
# parameters of interest
params_lcs_curio_curr_ppd <- broom::tidy(fit_mi_lcs_curio_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ ppd", "ppd ~~ ppd", "d_curio_1 ~ ppd", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.027 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.353 | NA | NA |
| d_curio_1 ~1 | 0.470 | -0.032 | 0.973 | 2.812 | 1.833 | 0.067 |
| curio_t1 ~1 | 3.862 | 3.582 | 4.142 | 7.925 | 27.031 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.026 | -0.017 | 0.068 | 0.920 | 1.185 | 0.236 |
| d_curio_1 ~ curio_t1 | -0.080 | -0.199 | 0.039 | -0.232 | -1.311 | 0.190 |
| d_curio_1 ~ ppd | -0.025 | -0.078 | 0.029 | -0.138 | -0.906 | 0.365 |
| ppd ~~ ppd | 0.863 | 0.742 | 0.983 | 1.000 | 14.003 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7939.886 | 8026.341 | 0.997 | 0.019 | 0.035 |
# parameters of interest
params_lcs_aesth_curr_ppd <- broom::tidy(fit_mi_lcs_aesth_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ ppd", "ppd ~~ ppd", "d_aesth_1 ~ ppd", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.979 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.510 | NA | NA |
| d_aesth_1 ~1 | 0.344 | 0.026 | 0.662 | 12.812 | 2.122 | 0.034 |
| aesth_t1 ~1 | 2.981 | 2.859 | 3.104 | 57.861 | 47.780 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.001 | 0.000 | 0.001 | 0.951 | 4.480 | 0.000 |
| d_aesth_1 ~ aesth_t1 | -0.113 | -0.219 | -0.007 | -0.216 | -2.093 | 0.036 |
| d_aesth_1 ~ ppd | -0.001 | -0.006 | 0.003 | -0.041 | -0.509 | 0.611 |
| ppd ~~ ppd | 0.863 | 0.742 | 0.983 | 1.000 | 14.006 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 317 | 23 | 7524.192 | 7610.647 | 0.976 | 0.048 | 0.051 |
# parameters of interest
params_lcs_imagi_curr_ppd <- broom::tidy(fit_mi_lcs_imagi_curr_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ ppd", "ppd ~~ ppd", "d_imagi_1 ~ ppd", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.990 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.496 | NA | NA |
| d_imagi_1 ~1 | 0.392 | -0.164 | 0.949 | 0.998 | 1.381 | 0.167 |
| imagi_t1 ~1 | 3.913 | 3.534 | 4.291 | 4.996 | 20.270 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.146 | 0.062 | 0.230 | 0.944 | 3.397 | 0.001 |
| d_imagi_1 ~ imagi_t1 | -0.111 | -0.232 | 0.009 | -0.222 | -1.806 | 0.071 |
| d_imagi_1 ~ ppd | 0.026 | -0.044 | 0.095 | 0.061 | 0.722 | 0.470 |
| ppd ~~ ppd | 0.863 | 0.742 | 0.983 | 1.000 | 14.003 | 0.000 |
Run models for all facets with a template & loop:
# create df for table:
df_table_ppd_skill <- bind_rows(
#traits
params_lcs_extra_curr_ppd %>% filter(term=="d_extra_1 ~ ppd"),
params_lcs_agree_curr_ppd %>% filter(term=="d_agree_1 ~ ppd"),
params_lcs_consc_curr_ppd %>% filter(term=="d_consc_1 ~ ppd"),
params_lcs_neuro_curr_ppd %>% filter(term=="d_neuro_1 ~ ppd"),
params_lcs_openn_curr_ppd %>% filter(term=="d_openn_1 ~ ppd"),
#facets
params_lcs_socia_curr_ppd %>% filter(term=="d_socia_1 ~ ppd"),
params_lcs_asser_curr_ppd %>% filter(term=="d_asser_1 ~ ppd"),
params_lcs_energ_curr_ppd %>% filter(term=="d_energ_1 ~ ppd"),
params_lcs_compa_curr_ppd %>% filter(term=="d_compa_1 ~ ppd"),
params_lcs_respe_curr_ppd %>% filter(term=="d_respe_1 ~ ppd"),
params_lcs_trust_curr_ppd %>% filter(term=="d_trust_1 ~ ppd"),
params_lcs_organ_curr_ppd %>% filter(term=="d_organ_1 ~ ppd"),
params_lcs_produ_curr_ppd %>% filter(term=="d_produ_1 ~ ppd"),
params_lcs_respo_curr_ppd %>% filter(term=="d_respo_1 ~ ppd"),
params_lcs_anxie_curr_ppd %>% filter(term=="d_anxie_1 ~ ppd"),
params_lcs_depre_curr_ppd %>% filter(term=="d_depre_1 ~ ppd"),
params_lcs_volat_curr_ppd %>% filter(term=="d_volat_1 ~ ppd"),
params_lcs_curio_curr_ppd %>% filter(term=="d_curio_1 ~ ppd"),
params_lcs_aesth_curr_ppd %>% filter(term=="d_aesth_1 ~ ppd"),
params_lcs_imagi_curr_ppd %>% filter(term=="d_imagi_1 ~ ppd")
) %>%
mutate(trait = names(b5_vars),
moderator = rep(c("ppd"), 20)) %>%
select(trait, moderator, estimate, std.all, statistic, p.value)Results summary across the Big Five traits: personal project dimensions (ppd) as moderators on the latent change score
kable(df_table_ppd_skill[1:5, ], digits = 3)| trait | moderator | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| extraversion | ppd | 0.056 | 0.158 | 2.082 | 0.037 |
| agreeableness | ppd | -0.014 | -0.061 | -0.776 | 0.438 |
| conscientiousness | ppd | 0.007 | 0.022 | 0.300 | 0.764 |
| neuroticism | ppd | -0.051 | -0.095 | -1.507 | 0.132 |
| openness | ppd | 0.004 | 0.020 | 0.204 | 0.838 |
Results summary across the Big Five facets: personal project dimensions (ppd) as moderators on the latent change score
kable(df_table_ppd_skill[6:20, ], digits = 3)| trait | moderator | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| sociability | ppd | 0.069 | 0.120 | 1.446 | 0.148 |
| assertiveness | ppd | 0.059 | 0.272 | 1.882 | 0.060 |
| energy | ppd | -0.018 | -0.065 | -0.702 | 0.483 |
| compassion | ppd | 0.040 | 0.176 | 1.230 | 0.219 |
| respectfulness | ppd | -0.039 | -0.114 | -1.551 | 0.121 |
| trust | ppd | -0.003 | -0.007 | -0.078 | 0.938 |
| organization | ppd | -0.042 | -0.075 | -1.036 | 0.300 |
| productiveness | ppd | -0.033 | -0.087 | -0.940 | 0.347 |
| responsibility | ppd | -0.038 | -0.177 | -1.605 | 0.108 |
| anxiety | ppd | 0.046 | 0.074 | 1.067 | 0.286 |
| depression | ppd | 0.056 | 0.135 | 1.760 | 0.078 |
| volatility | ppd | -0.014 | -0.025 | -0.336 | 0.737 |
| curiosity | ppd | -0.025 | -0.138 | -0.906 | 0.365 |
| aesthetic | ppd | -0.001 | -0.041 | -0.509 | 0.611 |
| imagination | ppd | 0.026 | 0.061 | 0.722 | 0.470 |
Reshape and split data set by intervention group:
df_sbsa_wide_pers_sa_ppd <- df_sbsa %>%
filter(rando=="Self-Acceptance") %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sa01"))) %>%
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with(c("sa01")))) %>%
select(-c(sa01_01_t2, sa01_02_t2))
# colnames(df_sbsa_wide_pers_sa_ppd)
group_assign <- df_sbsa %>% select(pid, rando) %>% unique()
df_sbsa_wide_pers_sa_ppd <- df_sbsa_wide_pers %>% left_join(group_assign) %>% filter(rando=="Self-Acceptance") %>% select(-rando) %>%
left_join(df_sbsa_wide_pers_sa_ppd)
# need to form a mean score because some models did not converge when using the latent factor of PPD (but high correlation between the two items)
df_sbsa_wide_pers_sa_ppd <- df_sbsa_wide_pers_sa_ppd %>% mutate(ppd = rowMeans(across(c(sa01_01_t1, sa01_02_t1)), na.rm=T))Run models for all traits with a template & loop:
# create templates
trait_template_ppd_accept <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadings
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~ ppd # This estimates the moderation effect on personality at T1
d_trait_1 ~ trait_t1 + ppd # This estimates the self-feedback parameter and the moderation effect on the change score
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ppd ~~ ppd
ppd ~ 1
'
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal (6 = ideal)
# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_ppd_accept,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"),
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_ppd")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_ppd")), trait_model_fit))
} Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 19 | 3393.677 | 3464.175 | 0.991 | 0.03 | 0.036 |
# parameters of interest
params_lcs_extra_ideal_ppd <- broom::tidy(fit_mi_lcs_extra_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ ppd", "ppd ~~ ppd", "extra_t1 ~ ppd", # acceptance ppd
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.049 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.785 | NA | NA |
| d_extra_1 ~1 | 1.254 | 0.695 | 1.813 | 4.755 | 4.398 | 0.000 |
| extra_t1 ~1 | 3.913 | 3.664 | 4.161 | 11.114 | 30.862 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.056 | 0.027 | 0.086 | 0.810 | 3.710 | 0.000 |
| extra_t1 ~ ppd | -0.018 | -0.081 | 0.044 | -0.041 | -0.578 | 0.564 |
| d_extra_1 ~ extra_t1 | -0.326 | -0.464 | -0.189 | -0.436 | -4.647 | 0.000 |
| d_extra_1 ~ ppd | -0.008 | -0.055 | 0.040 | -0.023 | -0.319 | 0.750 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.004 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 19 | 3595.852 | 3666.35 | 1 | 0 | 0.02 |
# parameters of interest
params_lcs_agree_ideal_ppd <- broom::tidy(fit_mi_lcs_agree_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ ppd", "ppd ~~ ppd", "agree_t1 ~ ppd", # acceptance ppd
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 0.954 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.617 | NA | NA |
| d_agree_1 ~1 | 0.651 | 0.211 | 1.090 | 2.205 | 2.903 | 0.004 |
| agree_t1 ~1 | 3.648 | 3.314 | 3.982 | 7.988 | 21.416 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.082 | 0.044 | 0.119 | 0.936 | 4.263 | 0.000 |
| agree_t1 ~ ppd | 0.076 | -0.008 | 0.160 | 0.131 | 1.774 | 0.076 |
| d_agree_1 ~ agree_t1 | -0.156 | -0.259 | -0.052 | -0.241 | -2.956 | 0.003 |
| d_agree_1 ~ ppd | -0.019 | -0.076 | 0.038 | -0.050 | -0.644 | 0.520 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.002 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 19 | 2977.869 | 3048.367 | 0.984 | 0.048 | 0.031 |
# parameters of interest
params_lcs_consc_ideal_ppd <- broom::tidy(fit_mi_lcs_consc_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ ppd", "ppd ~~ ppd", "consc_t1 ~ ppd", # acceptance ppd
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.746 | NA | NA |
| d_consc_1 ~1 | 1.391 | 0.693 | 2.088 | 4.463 | 3.907 | 0.000 |
| consc_t1 ~1 | 4.127 | 3.874 | 4.381 | 10.085 | 31.915 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.082 | 0.052 | 0.111 | 0.843 | 5.427 | 0.000 |
| consc_t1 ~ ppd | 0.070 | 0.005 | 0.135 | 0.134 | 2.107 | 0.035 |
| d_consc_1 ~ consc_t1 | -0.250 | -0.409 | -0.091 | -0.329 | -3.077 | 0.002 |
| d_consc_1 ~ ppd | -0.072 | -0.125 | -0.020 | -0.182 | -2.695 | 0.007 |
| ppd ~~ ppd | 0.617 | 0.517 | 0.718 | 1.000 | 12.005 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 19 | 3277.319 | 3347.817 | 0.996 | 0.021 | 0.031 |
# parameters of interest
params_lcs_neuro_ideal_ppd <- broom::tidy(fit_mi_lcs_neuro_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ ppd", "ppd ~~ ppd", "neuro_t1 ~ ppd", # acceptance ppd
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.898 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.770 | NA | NA |
| d_neuro_1 ~1 | 0.395 | 0.027 | 0.763 | 1.228 | 2.107 | 0.035 |
| neuro_t1 ~1 | 1.824 | 1.531 | 2.118 | 4.862 | 12.187 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.095 | 0.056 | 0.134 | 0.916 | 4.809 | 0.000 |
| neuro_t1 ~ ppd | -0.044 | -0.119 | 0.031 | -0.092 | -1.150 | 0.250 |
| d_neuro_1 ~ neuro_t1 | -0.247 | -0.422 | -0.071 | -0.287 | -2.756 | 0.006 |
| d_neuro_1 ~ ppd | 0.007 | -0.050 | 0.065 | 0.018 | 0.249 | 0.803 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.006 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 19 | 3451.024 | 3521.522 | 0.996 | 0.023 | 0.031 |
# parameters of interest
params_lcs_openn_ideal_ppd <- broom::tidy(fit_mi_lcs_openn_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ ppd", "ppd ~~ ppd", "openn_t1 ~ ppd", # acceptance ppd
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.954 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.670 | NA | NA |
| d_openn_1 ~1 | 0.750 | 0.195 | 1.306 | 2.696 | 2.646 | 0.008 |
| openn_t1 ~1 | 3.753 | 3.507 | 3.999 | 9.473 | 29.922 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.071 | 0.039 | 0.104 | 0.921 | 4.304 | 0.000 |
| openn_t1 ~ ppd | 0.038 | -0.024 | 0.100 | 0.076 | 1.200 | 0.230 |
| d_openn_1 ~ openn_t1 | -0.197 | -0.329 | -0.065 | -0.281 | -2.932 | 0.003 |
| d_openn_1 ~ ppd | -0.001 | -0.051 | 0.049 | -0.004 | -0.055 | 0.956 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.004 | 0.000 |
Run models for all facets with a template & loop:
# create templates:
# 1st, for facet-specific acceptance goal
facet_template_ppd_accept <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~ ppd # This estimates the moderation effect on personality at T1
d_facet_1 ~ facet_t1 + ppd # This estimates the self-feedback parameter and the moderation effect on the change score
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ppd ~~ ppd
ppd ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[6]], item_nrs)
template_filled <- str_replace_all(facet_template_ppd_accept,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa_wide_pers_sa_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_ppd")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_ppd")), facet_model_fit))
} Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7079.396 | 7164.736 | 0.944 | 0.054 | 0.054 |
# parameters of interest
params_lcs_socia_ideal_ppd <- broom::tidy(fit_mi_lcs_socia_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ ppd", "ppd ~~ ppd", "d_socia_1 ~ ppd", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.002 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.704 | NA | NA |
| d_socia_1 ~1 | 1.149 | 0.286 | 2.013 | 4.491 | 2.608 | 0.009 |
| socia_t1 ~1 | 4.349 | 4.068 | 4.631 | 11.942 | 30.325 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.057 | 0.008 | 0.106 | 0.866 | 2.274 | 0.023 |
| d_socia_1 ~ socia_t1 | -0.250 | -0.439 | -0.061 | -0.356 | -2.599 | 0.009 |
| d_socia_1 ~ ppd | -0.030 | -0.089 | 0.029 | -0.091 | -0.982 | 0.326 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.000 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7291.801 | 7377.14 | 0.977 | 0.033 | 0.04 |
# parameters of interest
params_lcs_asser_ideal_ppd <- broom::tidy(fit_mi_lcs_asser_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ ppd", "ppd ~~ ppd", "d_asser_1 ~ ppd", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.012 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.719 | NA | NA |
| d_asser_1 ~1 | 1.032 | 0.216 | 1.848 | 5.293 | 2.478 | 0.013 |
| asser_t1 ~1 | 4.283 | 4.065 | 4.501 | 15.622 | 38.518 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.033 | 0.002 | 0.063 | 0.861 | 2.105 | 0.035 |
| d_asser_1 ~ asser_t1 | -0.262 | -0.447 | -0.078 | -0.369 | -2.784 | 0.005 |
| d_asser_1 ~ ppd | 0.008 | -0.039 | 0.054 | 0.031 | 0.325 | 0.745 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.004 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 6617.322 | 6702.661 | 0.958 | 0.036 | 0.049 |
# parameters of interest
params_lcs_energ_ideal_ppd <- broom::tidy(fit_mi_lcs_energ_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ ppd", "ppd ~~ ppd", "d_energ_1 ~ ppd", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.093 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.750 | NA | NA |
| d_energ_1 ~1 | 0.770 | 0.305 | 1.236 | 7.241 | 3.246 | 0.001 |
| energ_t1 ~1 | 2.429 | 2.262 | 2.596 | 15.663 | 28.554 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.009 | -0.014 | 0.032 | 0.787 | 0.761 | 0.447 |
| d_energ_1 ~ energ_t1 | -0.317 | -0.509 | -0.126 | -0.462 | -3.249 | 0.001 |
| d_energ_1 ~ ppd | -0.003 | -0.025 | 0.019 | -0.020 | -0.242 | 0.809 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.004 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7568.07 | 7653.41 | 1 | 0 | 0.035 |
# parameters of interest
params_lcs_compa_ideal_ppd <- broom::tidy(fit_mi_lcs_compa_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ ppd", "ppd ~~ ppd", "d_compa_1 ~ ppd", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 0.938 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.564 | NA | NA |
| d_compa_1 ~1 | 0.416 | -0.572 | 1.404 | 1.143 | 0.825 | 0.409 |
| compa_t1 ~1 | 3.862 | 3.343 | 4.382 | 6.381 | 14.579 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.128 | 0.001 | 0.254 | 0.965 | 1.981 | 0.048 |
| d_compa_1 ~ compa_t1 | -0.113 | -0.330 | 0.104 | -0.188 | -1.020 | 0.308 |
| d_compa_1 ~ ppd | -0.002 | -0.116 | 0.113 | -0.003 | -0.026 | 0.979 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.006 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 6026.441 | 6111.781 | 0.974 | 0.037 | 0.048 |
# parameters of interest
params_lcs_respe_ideal_ppd <- broom::tidy(fit_mi_lcs_respe_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ ppd", "ppd ~~ ppd", "d_respe_1 ~ ppd", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.871 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.565 | NA | NA |
| d_respe_1 ~1 | 0.238 | -0.640 | 1.117 | 1.191 | 0.532 | 0.595 |
| respe_t1 ~1 | 4.585 | 4.331 | 4.838 | 14.844 | 35.500 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.040 | 0.010 | 0.069 | 0.994 | 2.641 | 0.008 |
| d_respe_1 ~ respe_t1 | -0.050 | -0.226 | 0.126 | -0.077 | -0.556 | 0.578 |
| d_respe_1 ~ ppd | -0.005 | -0.055 | 0.046 | -0.018 | -0.183 | 0.855 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.005 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7307.139 | 7392.479 | 0.979 | 0.033 | 0.04 |
# parameters of interest
params_lcs_trust_ideal_ppd <- broom::tidy(fit_mi_lcs_trust_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ ppd", "ppd ~~ ppd", "d_trust_1 ~ ppd", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.985 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.659 | NA | NA |
| d_trust_1 ~1 | 0.335 | -0.073 | 0.743 | 1.224 | 1.607 | 0.108 |
| trust_t1 ~1 | 2.147 | 1.806 | 2.488 | 5.259 | 12.331 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.067 | 0.020 | 0.114 | 0.898 | 2.806 | 0.005 |
| d_trust_1 ~ trust_t1 | -0.203 | -0.360 | -0.047 | -0.304 | -2.554 | 0.011 |
| d_trust_1 ~ ppd | 0.023 | -0.041 | 0.087 | 0.066 | 0.711 | 0.477 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.003 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 6144.79 | 6230.13 | 0.949 | 0.046 | 0.047 |
# parameters of interest
params_lcs_organ_ideal_ppd <- broom::tidy(fit_mi_lcs_organ_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ ppd", "ppd ~~ ppd", "d_organ_1 ~ ppd", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.013 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.789 | NA | NA |
| d_organ_1 ~1 | 0.307 | -0.262 | 0.875 | 1.360 | 1.058 | 0.290 |
| organ_t1 ~1 | 1.926 | 1.702 | 2.150 | 6.649 | 16.840 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.041 | 0.001 | 0.082 | 0.807 | 1.994 | 0.046 |
| d_organ_1 ~ organ_t1 | -0.292 | -0.580 | -0.005 | -0.375 | -1.994 | 0.046 |
| d_organ_1 ~ ppd | 0.049 | -0.004 | 0.102 | 0.172 | 1.826 | 0.068 |
| ppd ~~ ppd | 0.617 | 0.517 | 0.718 | 1.000 | 12.006 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 5750.074 | 5835.414 | 0.873 | 0.067 | 0.065 |
# parameters of interest
params_lcs_produ_ideal_ppd <- broom::tidy(fit_mi_lcs_produ_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ ppd", "ppd ~~ ppd", "d_produ_1 ~ ppd", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.959 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.634 | NA | NA |
| d_produ_1 ~1 | 0.103 | -0.364 | 0.570 | 0.488 | 0.432 | 0.666 |
| produ_t1 ~1 | 1.650 | 1.405 | 1.896 | 5.169 | 13.164 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.040 | 0.005 | 0.076 | 0.910 | 2.231 | 0.026 |
| d_produ_1 ~ produ_t1 | -0.171 | -0.440 | 0.098 | -0.259 | -1.247 | 0.212 |
| d_produ_1 ~ ppd | 0.038 | -0.013 | 0.090 | 0.143 | 1.466 | 0.143 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.004 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 6627.8 | 6713.14 | 0.986 | 0.025 | 0.042 |
# parameters of interest
params_lcs_respo_ideal_ppd <- broom::tidy(fit_mi_lcs_respo_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ ppd", "ppd ~~ ppd", "d_respo_1 ~ ppd", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.985 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.723 | NA | NA |
| d_respo_1 ~1 | 1.295 | 0.543 | 2.048 | 3.627 | 3.376 | 0.001 |
| respo_t1 ~1 | 4.010 | 3.603 | 4.417 | 8.244 | 19.311 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.110 | 0.039 | 0.181 | 0.863 | 3.043 | 0.002 |
| d_respo_1 ~ respo_t1 | -0.243 | -0.416 | -0.070 | -0.331 | -2.752 | 0.006 |
| d_respo_1 ~ ppd | -0.060 | -0.137 | 0.016 | -0.132 | -1.544 | 0.123 |
| ppd ~~ ppd | 0.617 | 0.517 | 0.718 | 1.000 | 12.006 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7258.723 | 7344.062 | 0.899 | 0.045 | 0.049 |
# parameters of interest
params_lcs_anxie_ideal_ppd <- broom::tidy(fit_mi_lcs_anxie_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ ppd", "ppd ~~ ppd", "d_anxie_1 ~ ppd", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.807 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.876 | NA | NA |
| d_anxie_1 ~1 | 1.486 | -0.679 | 3.651 | 4.706 | 1.345 | 0.179 |
| anxie_t1 ~1 | 4.423 | 4.116 | 4.730 | 15.196 | 28.205 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.091 | -0.021 | 0.203 | 0.909 | 1.586 | 0.113 |
| d_anxie_1 ~ anxie_t1 | -0.314 | -0.782 | 0.153 | -0.290 | -1.318 | 0.187 |
| d_anxie_1 ~ ppd | -0.022 | -0.124 | 0.080 | -0.055 | -0.426 | 0.670 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.719 | 1.000 | 12.001 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 5912.144 | 5997.484 | 0.873 | 0.074 | 0.068 |
# parameters of interest
params_lcs_depre_ideal_ppd <- broom::tidy(fit_mi_lcs_depre_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ ppd", "ppd ~~ ppd", "d_depre_1 ~ ppd", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.920 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.540 | NA | NA |
| d_depre_1 ~1 | 0.397 | -0.824 | 1.617 | 2.888 | 0.637 | 0.524 |
| depre_t1 ~1 | 4.327 | 4.119 | 4.536 | 18.482 | 40.719 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.018 | -0.007 | 0.044 | 0.975 | 1.409 | 0.159 |
| d_depre_1 ~ depre_t1 | -0.078 | -0.356 | 0.199 | -0.134 | -0.554 | 0.579 |
| d_depre_1 ~ ppd | -0.013 | -0.050 | 0.025 | -0.072 | -0.659 | 0.510 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.004 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 6783.917 | 6869.257 | 0.93 | 0.049 | 0.052 |
# parameters of interest
params_lcs_volat_ideal_ppd <- broom::tidy(fit_mi_lcs_volat_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ ppd", "ppd ~~ ppd", "d_volat_1 ~ ppd", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.834 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.691 | NA | NA |
| d_volat_1 ~1 | 0.242 | -0.351 | 0.835 | 0.693 | 0.800 | 0.424 |
| volat_t1 ~1 | 1.803 | 1.390 | 2.215 | 4.275 | 8.564 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.119 | 0.028 | 0.210 | 0.978 | 2.562 | 0.010 |
| d_volat_1 ~ volat_t1 | -0.124 | -0.387 | 0.139 | -0.149 | -0.921 | 0.357 |
| d_volat_1 ~ ppd | 0.003 | -0.092 | 0.098 | 0.006 | 0.056 | 0.955 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.001 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7148.526 | 7233.865 | 1 | 0 | 0.034 |
# parameters of interest
params_lcs_curio_ideal_ppd <- broom::tidy(fit_mi_lcs_curio_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ ppd", "ppd ~~ ppd", "d_curio_1 ~ ppd", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.977 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.623 | NA | NA |
| d_curio_1 ~1 | 0.691 | -0.393 | 1.775 | 3.386 | 1.249 | 0.212 |
| curio_t1 ~1 | 4.582 | 4.318 | 4.846 | 14.311 | 34.040 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.038 | -0.009 | 0.084 | 0.909 | 1.593 | 0.111 |
| d_curio_1 ~ curio_t1 | -0.177 | -0.402 | 0.048 | -0.277 | -1.541 | 0.123 |
| d_curio_1 ~ ppd | 0.028 | -0.034 | 0.091 | 0.109 | 0.882 | 0.378 |
| ppd ~~ ppd | 0.618 | 0.517 | 0.718 | 1.000 | 12.004 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 7185.814 | 7271.154 | 0.99 | 0.029 | 0.04 |
# parameters of interest
params_lcs_aesth_ideal_ppd <- broom::tidy(fit_mi_lcs_aesth_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ ppd", "ppd ~~ ppd", "d_aesth_1 ~ ppd", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.952 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.654 | NA | NA |
| d_aesth_1 ~1 | 0.588 | 0.117 | 1.059 | 13.083 | 2.449 | 0.014 |
| aesth_t1 ~1 | 3.106 | 2.957 | 3.255 | 47.454 | 40.879 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.002 | -0.006 | 0.010 | 0.922 | 0.437 | 0.662 |
| d_aesth_1 ~ aesth_t1 | -0.193 | -0.342 | -0.044 | -0.281 | -2.532 | 0.011 |
| d_aesth_1 ~ ppd | 0.005 | -0.008 | 0.017 | 0.079 | 0.723 | 0.470 |
| ppd ~~ ppd | 0.617 | 0.517 | 0.718 | 1.000 | 12.011 | 0.000 |
Results summary (ppd = personal project dimensions):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_ppd) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 302 | 23 | 6648.853 | 6734.192 | 0.964 | 0.035 | 0.051 |
# parameters of interest
params_lcs_imagi_ideal_ppd <- broom::tidy(fit_mi_lcs_imagi_ideal_ppd, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ ppd", "ppd ~~ ppd", "d_imagi_1 ~ ppd", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_ppd, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 0.908 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.601 | NA | NA |
| d_imagi_1 ~1 | 0.735 | -0.505 | 1.975 | 3.226 | 1.161 | 0.245 |
| imagi_t1 ~1 | 4.493 | 4.220 | 4.765 | 13.050 | 32.346 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.046 | 0.006 | 0.087 | 0.894 | 2.244 | 0.025 |
| d_imagi_1 ~ imagi_t1 | -0.100 | -0.363 | 0.163 | -0.151 | -0.742 | 0.458 |
| d_imagi_1 ~ ppd | -0.081 | -0.145 | -0.017 | -0.278 | -2.479 | 0.013 |
| ppd ~~ ppd | 0.617 | 0.517 | 0.718 | 1.000 | 12.004 | 0.000 |
Run models for all facets with a template & loop:
# create df for table:
df_table_ppd_accept <- bind_rows(
#traits
params_lcs_extra_ideal_ppd %>% filter(term=="d_extra_1 ~ ppd"),
params_lcs_agree_ideal_ppd %>% filter(term=="d_agree_1 ~ ppd"),
params_lcs_consc_ideal_ppd %>% filter(term=="d_consc_1 ~ ppd"),
params_lcs_neuro_ideal_ppd %>% filter(term=="d_neuro_1 ~ ppd"),
params_lcs_openn_ideal_ppd %>% filter(term=="d_openn_1 ~ ppd"),
#facets
params_lcs_socia_ideal_ppd %>% filter(term=="d_socia_1 ~ ppd"),
params_lcs_asser_ideal_ppd %>% filter(term=="d_asser_1 ~ ppd"),
params_lcs_energ_ideal_ppd %>% filter(term=="d_energ_1 ~ ppd"),
params_lcs_compa_ideal_ppd %>% filter(term=="d_compa_1 ~ ppd"),
params_lcs_respe_ideal_ppd %>% filter(term=="d_respe_1 ~ ppd"),
params_lcs_trust_ideal_ppd %>% filter(term=="d_trust_1 ~ ppd"),
params_lcs_organ_ideal_ppd %>% filter(term=="d_organ_1 ~ ppd"),
params_lcs_produ_ideal_ppd %>% filter(term=="d_produ_1 ~ ppd"),
params_lcs_respo_ideal_ppd %>% filter(term=="d_respo_1 ~ ppd"),
params_lcs_anxie_ideal_ppd %>% filter(term=="d_anxie_1 ~ ppd"),
params_lcs_depre_ideal_ppd %>% filter(term=="d_depre_1 ~ ppd"),
params_lcs_volat_ideal_ppd %>% filter(term=="d_volat_1 ~ ppd"),
params_lcs_curio_ideal_ppd %>% filter(term=="d_curio_1 ~ ppd"),
params_lcs_aesth_ideal_ppd %>% filter(term=="d_aesth_1 ~ ppd"),
params_lcs_imagi_ideal_ppd %>% filter(term=="d_imagi_1 ~ ppd")
) %>%
mutate(trait = names(b5_vars),
moderator = rep(c("ppd"), 20)) %>%
select(trait, moderator, estimate, std.all, statistic, p.value)Results summary across the Big Five traits: personal project dimensions (ppd) as moderators on the latent change score
kable(df_table_ppd_accept[1:5, ], digits = 3)| trait | moderator | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| extraversion | ppd | -0.008 | -0.023 | -0.319 | 0.750 |
| agreeableness | ppd | -0.019 | -0.050 | -0.644 | 0.520 |
| conscientiousness | ppd | -0.072 | -0.182 | -2.695 | 0.007 |
| neuroticism | ppd | 0.007 | 0.018 | 0.249 | 0.803 |
| openness | ppd | -0.001 | -0.004 | -0.055 | 0.956 |
Results summary across the Big Five facets: personal project dimensions (ppd) as moderators on the latent change score
kable(df_table_ppd_accept[6:20, ], digits = 3)| trait | moderator | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| sociability | ppd | -0.030 | -0.091 | -0.982 | 0.326 |
| assertiveness | ppd | 0.008 | 0.031 | 0.325 | 0.745 |
| energy | ppd | -0.003 | -0.020 | -0.242 | 0.809 |
| compassion | ppd | -0.002 | -0.003 | -0.026 | 0.979 |
| respectfulness | ppd | -0.005 | -0.018 | -0.183 | 0.855 |
| trust | ppd | 0.023 | 0.066 | 0.711 | 0.477 |
| organization | ppd | 0.049 | 0.172 | 1.826 | 0.068 |
| productiveness | ppd | 0.038 | 0.143 | 1.466 | 0.143 |
| responsibility | ppd | -0.060 | -0.132 | -1.544 | 0.123 |
| anxiety | ppd | -0.022 | -0.055 | -0.426 | 0.670 |
| depression | ppd | -0.013 | -0.072 | -0.659 | 0.510 |
| volatility | ppd | 0.003 | 0.006 | 0.056 | 0.955 |
| curiosity | ppd | 0.028 | 0.109 | 0.882 | 0.378 |
| aesthetic | ppd | 0.005 | 0.079 | 0.723 | 0.470 |
| imagination | ppd | -0.081 | -0.278 | -2.479 | 0.013 |
Prepare data frame for plotting:
df_table_ppd_plot1 <- bind_rows(
#traits
params_lcs_extra_curr_ppd %>% filter(term=="d_extra_1 ~ ppd"),
params_lcs_agree_curr_ppd %>% filter(term=="d_agree_1 ~ ppd"),
params_lcs_consc_curr_ppd %>% filter(term=="d_consc_1 ~ ppd"),
params_lcs_neuro_curr_ppd %>% filter(term=="d_neuro_1 ~ ppd"),
params_lcs_openn_curr_ppd %>% filter(term=="d_openn_1 ~ ppd"),
#facets
params_lcs_socia_curr_ppd %>% filter(term=="d_socia_1 ~ ppd"),
params_lcs_asser_curr_ppd %>% filter(term=="d_asser_1 ~ ppd"),
params_lcs_energ_curr_ppd %>% filter(term=="d_energ_1 ~ ppd"),
params_lcs_compa_curr_ppd %>% filter(term=="d_compa_1 ~ ppd"),
params_lcs_respe_curr_ppd %>% filter(term=="d_respe_1 ~ ppd"),
params_lcs_trust_curr_ppd %>% filter(term=="d_trust_1 ~ ppd"),
params_lcs_organ_curr_ppd %>% filter(term=="d_organ_1 ~ ppd"),
params_lcs_produ_curr_ppd %>% filter(term=="d_produ_1 ~ ppd"),
params_lcs_respo_curr_ppd %>% filter(term=="d_respo_1 ~ ppd"),
params_lcs_anxie_curr_ppd %>% filter(term=="d_anxie_1 ~ ppd"),
params_lcs_depre_curr_ppd %>% filter(term=="d_depre_1 ~ ppd"),
params_lcs_volat_curr_ppd %>% filter(term=="d_volat_1 ~ ppd"),
params_lcs_curio_curr_ppd %>% filter(term=="d_curio_1 ~ ppd"),
params_lcs_aesth_curr_ppd %>% filter(term=="d_aesth_1 ~ ppd"),
params_lcs_imagi_curr_ppd %>% filter(term=="d_imagi_1 ~ ppd")
) %>%
mutate(trait = names(b5_vars),
moderator = rep(c("ppd"), 20),
ref = "skill-building") %>%
select(trait, moderator, ref, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_ppd_plot2 <- bind_rows(
#traits
params_lcs_extra_ideal_ppd %>% filter(term=="d_extra_1 ~ ppd"),
params_lcs_agree_ideal_ppd %>% filter(term=="d_agree_1 ~ ppd"),
params_lcs_consc_ideal_ppd %>% filter(term=="d_consc_1 ~ ppd"),
params_lcs_neuro_ideal_ppd %>% filter(term=="d_neuro_1 ~ ppd"),
params_lcs_openn_ideal_ppd %>% filter(term=="d_openn_1 ~ ppd"),
#facets
params_lcs_socia_ideal_ppd %>% filter(term=="d_socia_1 ~ ppd"),
params_lcs_asser_ideal_ppd %>% filter(term=="d_asser_1 ~ ppd"),
params_lcs_energ_ideal_ppd %>% filter(term=="d_energ_1 ~ ppd"),
params_lcs_compa_ideal_ppd %>% filter(term=="d_compa_1 ~ ppd"),
params_lcs_respe_ideal_ppd %>% filter(term=="d_respe_1 ~ ppd"),
params_lcs_trust_ideal_ppd %>% filter(term=="d_trust_1 ~ ppd"),
params_lcs_organ_ideal_ppd %>% filter(term=="d_organ_1 ~ ppd"),
params_lcs_produ_ideal_ppd %>% filter(term=="d_produ_1 ~ ppd"),
params_lcs_respo_ideal_ppd %>% filter(term=="d_respo_1 ~ ppd"),
params_lcs_anxie_ideal_ppd %>% filter(term=="d_anxie_1 ~ ppd"),
params_lcs_depre_ideal_ppd %>% filter(term=="d_depre_1 ~ ppd"),
params_lcs_volat_ideal_ppd %>% filter(term=="d_volat_1 ~ ppd"),
params_lcs_curio_ideal_ppd %>% filter(term=="d_curio_1 ~ ppd"),
params_lcs_aesth_ideal_ppd %>% filter(term=="d_aesth_1 ~ ppd"),
params_lcs_imagi_ideal_ppd %>% filter(term=="d_imagi_1 ~ ppd")
) %>%
mutate(trait = names(b5_vars),
moderator = rep(c("ppd"), 20),
ref = "self-acceptance") %>%
select(trait, moderator, ref, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_ppd_plot <- bind_rows(df_table_ppd_plot1, df_table_ppd_plot2)
df_table_ppd_plot <- df_table_ppd_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref2 = factor(ref, levels = c("skill-building", "self-acceptance"), labels = c("skill-building", "self-acceptance"))) %>%
mutate(moderator = factor("personal project dimensions", levels = c("personal project dimensions"), labels = c("personal project dimensions"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_ppd_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ moderator + fct_rev(ref), ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
We will explore c) whether there are stronger rates of change/acceptance on traits that were selected as change goals than those that were not.
TBD!